subject:( Gaussian Process Optimization)

University of Waikato

1. Ma, Jinjin. Parameter Tuning Using Gaussian Processes .

Degree: 2012, University of Waikato

► Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good… (more)

▼ Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor modelling. Hence, it is necessary to acquire the “best” parameter values for a particular algorithm before building the model. The “best” model not only reflects the “real” function and is well fitted to existing points, but also gives good performance when making predictions for new points with previously unseen values. A number of methods exist that have been proposed to optimize parameter values. The basic idea of all such methods is a trial-and-error process whereas the work presented in this thesis employs Gaussian process (GP) regression to optimize the parameter values of a given machine learning algorithm. In this thesis, we consider the optimization of only two-parameter learning algorithms. All the possible parameter values are specified in a 2-dimensional grid in this work. To avoid brute-force search, Gaussian Process Optimization (GPO) makes use of “expected improvement” to pick useful points rather than validating every point of the grid step by step. The point with the highest expected improvement is evaluated using cross-validation and the resulting data point is added to the training set for the Gaussian process model. This process is repeated until a stopping criterion is met. The final model is built using the learning algorithm based on the best parameter values identified in this process. In order to test the effectiveness of this optimization method on regression and classification problems, we use it to optimize parameters of some well-known machine learning algorithms, such as decision tree learning, support vector machines and boosting with trees. Through the analysis of experimental results obtained on datasets from the UCI repository, we find that the GPO algorithm yields competitive performance compared with a brute-force approach, while exhibiting a distinct advantage in terms of training time and number of cross-validation runs. Overall, the GPO method is a promising method for the optimization of parameter values in machine learning. Advisors/Committee Members: Frank, Eibe (advisor), Holmes, Geoffrey (advisor).

Subjects/Keywords: Parameter Tunning; Gaussian Process Optimization; Machine Learning

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APA (6^th Edition):

Ma, J. (2012). Parameter Tuning Using Gaussian Processes . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/6497

Chicago Manual of Style (16^th Edition):

Ma, Jinjin. “Parameter Tuning Using Gaussian Processes .” 2012. Masters Thesis, University of Waikato. Accessed December 07, 2019. http://hdl.handle.net/10289/6497.

MLA Handbook (7^th Edition):

Ma, Jinjin. “Parameter Tuning Using Gaussian Processes .” 2012. Web. 07 Dec 2019.

Vancouver:

Ma J. Parameter Tuning Using Gaussian Processes . [Internet] [Masters thesis]. University of Waikato; 2012. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10289/6497.

Council of Science Editors:

Ma J. Parameter Tuning Using Gaussian Processes . [Masters Thesis]. University of Waikato; 2012. Available from: http://hdl.handle.net/10289/6497

Penn State University

2. Alshraideh, Hussam. Analysis and Optimization of Profile and Shape Response Experiments.

Degree: PhD, Industrial Engineering, 2011, Penn State University

URL: https://etda.libraries.psu.edu/catalog/12126

► An engineering process that exhibits a response in the form of a univariate (or one-dimensional) curve whenever new experimental conditions are tried is said to… (more)

▼ An engineering process that exhibits a response in the form of a univariate (or one-dimensional) curve whenever new experimental conditions are tried is said to have a profile, or functional response. Likewise, a manufacturing process or engineering system where the response of interest is the geometry of a product or part is said to have a shape response. A shape response can relate to a planar (two-dimensional) geometrical feature, or to a three-dimensional one. The overall theme of this dissertation is the modeling and optimization of engineering processes that have either a profile or a shape response. The models and methods described in this dissertation have application mainly in manufacturing, engineering design, and computer experiments. Statistical Shape Analysis (SSA) is a relatively new area within Statistics. Traditionally the realm of biological applications, it has been recently applied to manufacturing problems. D.G. Kendall, in pioneering work conducted in the 1980’s, defined the shape of an object as the geometrical information that remains once certain similarity transformations, namely, rotations excluding reflections, translations, and dilatations (or dilations) are filtered out. His work is based on a landmark representation of an object, where a landmark consists of the coordinates of a point measured on the object together with a label, with labels that correspond from object to object. This representation turns out to be relevant in manufacturing, since data obtained using a coordinate measuring machine will typically have this appearance. Over the last 20 years, several SSA tests have been proposed to detect differences in the mean shape between objects, but little work exists on the relative merits of these methods. The first part of this dissertation consists of a comprehensive performance analysis of landmark-based tests for mean shape differences. Since the performance of these tests depends on the types of shapes being tested, we consider both shapes that have been studied in the scarce extant literature on the subject, namely triangles and arbitrary polygons with few landmarks, and also consider shapes of specific interest in manufacturing applications, such as circular and cylindrical geometries with tens to hundreds of landmarks. An additional problem studied in this dissertation is that of shape optimization, that is, find the best operating conditions that lead to the most desirable shape of the product under fabrication. Previous tests for shape differences are based on Kendall’s definition of shape, which neglects differences in size between objects since it removes dilation (scale) effects, and make up for this deficiency by testing separately for differences in size. As an alternative, we present statistical tests for differences in form between the objects, where we define the form of an object as the geometrical information that remains once the effect of rotations and translations, but not dilations, is filtered out. We further develop a form optimization method when noise…

Subjects/Keywords: Profile; Shape; Gaussian Process; Geometry; shape optimization; Statistical shape analysis; process optimization.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Alshraideh, H. (2011). Analysis and Optimization of Profile and Shape Response Experiments. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12126

Chicago Manual of Style (16^th Edition):

Alshraideh, Hussam. “Analysis and Optimization of Profile and Shape Response Experiments.” 2011. Doctoral Dissertation, Penn State University. Accessed December 07, 2019. https://etda.libraries.psu.edu/catalog/12126.

MLA Handbook (7^th Edition):

Alshraideh, Hussam. “Analysis and Optimization of Profile and Shape Response Experiments.” 2011. Web. 07 Dec 2019.

Vancouver:

Alshraideh H. Analysis and Optimization of Profile and Shape Response Experiments. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2019 Dec 07]. Available from: https://etda.libraries.psu.edu/catalog/12126.

Council of Science Editors:

Alshraideh H. Analysis and Optimization of Profile and Shape Response Experiments. [Doctoral Dissertation]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/12126

University of Alberta

3. Ranjan, Rishik. Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization.

Degree: MS, Department of Chemical and Materials Engineering, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/b2773z58w

► Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a… (more)

▼ Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a relatively new Bayesian approach to non-parametric data based modelling is investigated in detail. One of the primary concerns regarding the application of such methods is their sensitivity to the presence of outlying observations. Another concern is that their ability to predict beyond the range of observed data is often poor which can limit their applicability. Both of these issues are explored in this work. The problem of sensitivity to outliers is dealt with by using a robust GP regression model. The common approach in literature for identification of this model is to approximate the marginal likelihood and maximize it using conjugate gradient algorithm. In this work, an EM algorithm based approach is proposed in which an approximate lower bound on the marginal likelihood is iteratively maximized. Models identified using this method are compared against those identified using conjugate gradient method in terms of prediction performance on many synthetic and real benchmark datasets. It is observed that the two approaches are similar in prediction performance. However the advantages of EM approach are numerical stability, ease of implementation and theoretical guarantee of convergence. The application of proposed robust GP regression in chemical engineering is also explored. An optimization problem for an industrial water treatment and steam generation network is formulated. Process models are constructed using material balance equations and used for data reconciliation and steady state optimization of the cost of steam production. Since the overall network is under manual operation, a dynamic optimization framework is constructed to find a set point change strategy which operators can use for minimizing steam production cost. Dynamic models for process units and tanks are integrated into this framework. Some of these models are identified using proposed robust GP regression method. Extrapolation ability of identified GP models is improved by applying a suitable GP kernel structure and by using some ad hoc scaling techniques. Based on the application of robust GP regression to an industrial optimization problem, it is shown that non-parametric data-based modelling can be successfully integrated with process optimization objectives.

Subjects/Keywords: SAGD; Optimization; Outliers; Robust identification; Gaussian process regression; EM algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ranjan, R. (2015). Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/b2773z58w

Chicago Manual of Style (16^th Edition):

Ranjan, Rishik. “Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization.” 2015. Masters Thesis, University of Alberta. Accessed December 07, 2019. https://era.library.ualberta.ca/files/b2773z58w.

MLA Handbook (7^th Edition):

Ranjan, Rishik. “Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization.” 2015. Web. 07 Dec 2019.

Vancouver:

Ranjan R. Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Dec 07]. Available from: https://era.library.ualberta.ca/files/b2773z58w.

Council of Science Editors:

Ranjan R. Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/b2773z58w

Virginia Tech

4. Zielinski, Jacob Jonathan. Adapting Response Surface Methods for the Optimization of Black-Box Systems.

Degree: PhD, Statistics, 2010, Virginia Tech

URL: http://hdl.handle.net/10919/39295

► Complex mathematical models are often built to describe a physical process that would otherwise be extremely difficult, too costly or sometimes impossible to analyze. Generally,… (more)

▼ Complex mathematical models are often built to describe a physical process that would otherwise be extremely difficult, too costly or sometimes impossible to analyze. Generally, these models require solutions to many partial differential equations. As a result, the computer codes may take a considerable amount of time to complete a single evaluation. A time tested method of analysis for such models is Monte Carlo simulation. These simulations, however, often require many model evaluations, making this approach too computationally expensive. To limit the number of experimental runs, it is common practice to model the departure as a Gaussian stochastic process (GaSP) to develop an emulator of the computer model. One advantage for using an emulator is that once a GaSP is fit to realized outcomes, the computer model is easy to predict in unsampled regions of the input space. This is an attempt to 'characterize' the overall model of the computer code. Most of the historical work on design and analysis of computer experiments focus on the characterization of the computer model over a large region of interest. However, many practitioners seek other objectives, such as input screening (Welch et al., 1992), mapping a response surface, or optimization (Jones et al., 1998). Only recently have researchers begun to consider these topics in the design and analysis of computer experiments. In this dissertation, we explore a more traditional response surface approach (Myers, Montgomery and Anderson-Cook, 2009) in conjunction with traditional computer experiment methods to search for the optimum response of a process. For global optimization, Jones, Schonlau, and Welch's (1998) Efficient Global Optimization (EGO) algorithm remains a benchmark for subsequent research of computer experiments. We compare the proposed method in this paper to this leading benchmark. Our goal is to show that response surface methods can be effective means towards estimating an optimum response in the computer experiment framework. Advisors/Committee Members: Vining, Gordon Geoffrey (committeechair), Patterson, Angela N. (committee member), House, Leanna L. (committee member), Birch, Jeffrey B. (committee member).

Subjects/Keywords: Optimization; Gaussian Stochastic Process; Computer Experiments; Bayesian; Response Surface; DACE; Kriging

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zielinski, J. J. (2010). Adapting Response Surface Methods for the Optimization of Black-Box Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/39295

Chicago Manual of Style (16^th Edition):

Zielinski, Jacob Jonathan. “Adapting Response Surface Methods for the Optimization of Black-Box Systems.” 2010. Doctoral Dissertation, Virginia Tech. Accessed December 07, 2019. http://hdl.handle.net/10919/39295.

MLA Handbook (7^th Edition):

Zielinski, Jacob Jonathan. “Adapting Response Surface Methods for the Optimization of Black-Box Systems.” 2010. Web. 07 Dec 2019.

Vancouver:

Zielinski JJ. Adapting Response Surface Methods for the Optimization of Black-Box Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10919/39295.

Council of Science Editors:

Zielinski JJ. Adapting Response Surface Methods for the Optimization of Black-Box Systems. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/39295

5. Contal, Emile. Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization.

Degree: Docteur es, Mathématiques appliquées, 2016, Paris Saclay

URL: http://www.theses.fr/2016SACLN038

►

Cette thèse se consacre à une analyse rigoureuse des algorithmes d'optimisation globale équentielle. On se place dans un modèle de bandits stochastiques où un agent… (more)

▼

Cette thèse se consacre à une analyse rigoureuse des algorithmes d'optimisation globale équentielle. On se place dans un modèle de bandits stochastiques où un agent vise à déterminer l'entrée d'un système optimisant un critère. Cette fonction cible n'est pas connue et l'agent effectue séquentiellement des requêtes pour évaluer sa valeur aux entrées qu'il choisit. Cette fonction peut ne pas être convexe et contenir un grand nombre d'optima locaux. Nous abordons le cas difficile où les évaluations sont coûteuses, ce qui exige de concevoir une sélection rigoureuse des requêtes. Nous considérons deux objectifs, d'une part l'optimisation de la somme des valeurs reçues à chaque itération, d'autre part l'optimisation de la meilleure valeur trouvée jusqu'à présent. Cette thèse s'inscrit dans le cadre de l'optimisation bayésienne lorsque la fonction est une réalisation d'un processus stochastique connu, et introduit également une nouvelle approche d'optimisation par ordonnancement où l'on effectue seulement des comparaisons des valeurs de la fonction. Nous proposons des algorithmes nouveaux et apportons des concepts théoriques pour obtenir des garanties de performance. Nous donnons une stratégie d'optimisation qui s'adapte à des observations reçues par batch et non individuellement. Une étude générique des supremums locaux de processus stochastiques nous permet d'analyser l'optimisation bayésienne sur des espaces de recherche nonparamétriques. Nous montrons également que notre approche s'étend à des processus naturels non gaussiens. Nous établissons des liens entre l'apprentissage actif et l'apprentissage statistique d'ordonnancements et déduisons un algorithme d'optimisation de fonctions potentiellement discontinue.

This dissertation is dedicated to a rigorous analysis of sequential global optimization algorithms. We consider the stochastic bandit model where an agent aim at finding the input of a given system optimizing the output. The function which links the input to the output is not explicit, the agent requests sequentially an oracle to evaluate the output for any input. This function is not supposed to be convex and may display many local optima. In this work we tackle the challenging case where the evaluations are expensive, which requires to design a careful selection of the input to evaluate. We study two different goals, either to maximize the sum of the rewards received at each iteration, or to maximize the best reward found so far. The present thesis comprises the field of global optimization where the function is a realization from a known stochastic process, and the novel field of optimization by ranking where we only perform function value comparisons. We propose novel algorithms and provide theoretical concepts leading to performance guarantees. We first introduce an optimization strategy for observations received by batch instead of individually. A generic study of local supremum of stochastic processes allows to analyze Bayesian optimization on nonparametric search spaces. In addition, we show that our…

Advisors/Committee Members: Vayatis, Nicolas (thesis director).

Subjects/Keywords: Apprentissage statistique; Optimisation; Processus gaussien; Statistical learning; Optimization; Gaussian process

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Contal, E. (2016). Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization. (Doctoral Dissertation). Paris Saclay. Retrieved from http://www.theses.fr/2016SACLN038

Chicago Manual of Style (16^th Edition):

Contal, Emile. “Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization.” 2016. Doctoral Dissertation, Paris Saclay. Accessed December 07, 2019. http://www.theses.fr/2016SACLN038.

MLA Handbook (7^th Edition):

Contal, Emile. “Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization.” 2016. Web. 07 Dec 2019.

Vancouver:

Contal E. Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization. [Internet] [Doctoral dissertation]. Paris Saclay; 2016. [cited 2019 Dec 07]. Available from: http://www.theses.fr/2016SACLN038.

Council of Science Editors:

Contal E. Méthodes d’apprentissage statistique pour l’optimisation globale : Statistical learning approaches for global optimization. [Doctoral Dissertation]. Paris Saclay; 2016. Available from: http://www.theses.fr/2016SACLN038

The Ohio State University

6. Bautista, Dianne Carrol Tan. A Sequential Design for Approximating the Pareto Front using the Expected Pareto Improvement Function.

Degree: PhD, Statistics, 2009, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1237600537

► We propose a methodology for the simultaneous optimization of multiple goal functions evaluated by a numerically intensive computer model. In a black box multiobjective problem,… (more)

Subjects/Keywords: Statistics; Multiobjective optimization; simulation-based optimization; Pareto optimality; multivariate emulation; Gaussian process; expected improvement

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APA (6^th Edition):

Bautista, D. C. T. (2009). A Sequential Design for Approximating the Pareto Front using the Expected Pareto Improvement Function. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1237600537

Chicago Manual of Style (16^th Edition):

Bautista, Dianne Carrol Tan. “A Sequential Design for Approximating the Pareto Front using the Expected Pareto Improvement Function.” 2009. Doctoral Dissertation, The Ohio State University. Accessed December 07, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1237600537.

MLA Handbook (7^th Edition):

Bautista, Dianne Carrol Tan. “A Sequential Design for Approximating the Pareto Front using the Expected Pareto Improvement Function.” 2009. Web. 07 Dec 2019.

Vancouver:

Bautista DCT. A Sequential Design for Approximating the Pareto Front using the Expected Pareto Improvement Function. [Internet] [Doctoral dissertation]. The Ohio State University; 2009. [cited 2019 Dec 07]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1237600537.

Council of Science Editors:

Bautista DCT. A Sequential Design for Approximating the Pareto Front using the Expected Pareto Improvement Function. [Doctoral Dissertation]. The Ohio State University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1237600537

Duke University

7. Jarrett, Nicholas Walton Daniel. Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization .

Degree: 2015, Duke University

URL: http://hdl.handle.net/10161/9974

► This thesis presents data analysis and methodology for two prediction problems. The first problem is forecasting midlife credit ratings from personality information collected during… (more)

Subjects/Keywords: Statistics; Computer science; Psychology; Cloud computing; Deployment optimization; Gaussian process; Matrix multiplication; Nonlinear prediction; Optimization

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APA (6^th Edition):

Jarrett, N. W. D. (2015). Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/9974

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Jarrett, Nicholas Walton Daniel. “Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization .” 2015. Thesis, Duke University. Accessed December 07, 2019. http://hdl.handle.net/10161/9974.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Jarrett, Nicholas Walton Daniel. “Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization .” 2015. Web. 07 Dec 2019.

Vancouver:

Jarrett NWD. Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization . [Internet] [Thesis]. Duke University; 2015. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10161/9974.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jarrett NWD. Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization . [Thesis]. Duke University; 2015. Available from: http://hdl.handle.net/10161/9974

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Cornell University

8. Eriksson, David. Scalable kernel methods and their use in black-box optimization .

Degree: 2018, Cornell University

URL: http://hdl.handle.net/1813/64846

► This dissertation uses structured linear algebra to scale kernel regression methods based on Gaussian processes (GPs) and radial basis function (RBF) interpolation to large, high-dimensional… (more)

▼ This dissertation uses structured linear algebra to scale kernel regression methods based on Gaussian processes (GPs) and radial basis function (RBF) interpolation to large, high-dimensional datasets. While kernel methods provide a general, principled framework for approximating functions from scattered data, they are often seen as impractical for large data sets as the standard approach to model fitting scales cubically with the number of data points. We introduce RBFs in Section 1.3 and GPs in Section 1.4. Chapter 2 develops novel O(n) approaches for GP regression with n points using fast approximate matrix vector multiplications (MVMs). Kernel learning with GPs require solving linear systems and computing the log determinant of an n x n kernel matrix. We use iterative methods relying on the fast MVMs to solve the linear systems and leverage stochastic approximations based on Chebyshev and Lanczos to approximate the log determinant. We find that Lanczos is generally highly efficient and accurate and superior to Chebyshev for kernel learning. We consider a large variety of experiments to demonstrate the generality of this approach. Chapter 3 extends the ideas from Chapter 3 to fitting a GP to both function values and derivatives. This requires linear solves and log determinants with an n(d+1) x n(d+1) kernel matrix in d dimensions, leading to O(n³ d³) computations for standard methods. We extend the previous methods and introduce a pivoted Cholesky preconditioner that cuts the iterations to convergence by several orders of magnitude. Our approaches, together with dimensionality reduction, lets us scale Bayesian optimization with derivatives to high-dimensional problems and large evaluation budgets. We introduce surrogate optimization in Section 1.5. Surrogate optimization is a key application of GPs and RBFs, where they are used to model a computationally-expensive black-box function based on previous evaluations. Chapter 4 introduces a global optimization algorithm for computationally expensive black-box function based on RBFs. Given an upper bound on the semi-norm of the objective function in a reproducing kernel Hilbert space associated with the RBF, we prove that our algorithm is globally convergent even though it may not sample densely. We discuss expected convergence rates and illustrate the performance of the method via experiments on a set of test problems. Chapter 5 describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of computationally expensive black-box functions where concurrent function evaluations are appealing. pySOT is a collection of synchronous and asynchronous surrogate optimization strategies, implemented in the POAP framework. The pySOT framework includes a variety of surrogate models, experimental designs, optimization strategies, test problems, and serves as a useful platform to compare… Advisors/Committee Members: Van Loan, Charles Francis (committeeMember), Shoemaker, Christine Ann (committeeMember), Townsend, Alex John (committeeMember).

Subjects/Keywords: Gaussian Process; Applied mathematics; Global Optimization; Radial Basis Function; Scalable Machine Learning; Surrogate Optimization; Mathematics; Computer science; Bayesian optimization

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APA (6^th Edition):

Eriksson, D. (2018). Scalable kernel methods and their use in black-box optimization . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/64846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Eriksson, David. “Scalable kernel methods and their use in black-box optimization .” 2018. Thesis, Cornell University. Accessed December 07, 2019. http://hdl.handle.net/1813/64846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Eriksson, David. “Scalable kernel methods and their use in black-box optimization .” 2018. Web. 07 Dec 2019.

Vancouver:

Eriksson D. Scalable kernel methods and their use in black-box optimization . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/1813/64846.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Eriksson D. Scalable kernel methods and their use in black-box optimization . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/64846

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New Mexico

9. Olson, Sterling. Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization.

Degree: Mechanical Engineering, 2019, University of New Mexico

URL: https://digitalrepository.unm.edu/me_etds/165

► Converting energy from the currents found within tidal channels, open ocean, rivers, and canals is a promising yet untapped source of renewable energy. In… (more)

Subjects/Keywords: metamodel; current energy converters; actuator disc; Gaussian process regression; optimization; Mechanical Engineering

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APA (6^th Edition):

Olson, S. (2019). Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization. (Masters Thesis). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/me_etds/165

Chicago Manual of Style (16^th Edition):

Olson, Sterling. “Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization.” 2019. Masters Thesis, University of New Mexico. Accessed December 07, 2019. https://digitalrepository.unm.edu/me_etds/165.

MLA Handbook (7^th Edition):

Olson, Sterling. “Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization.” 2019. Web. 07 Dec 2019.

Vancouver:

Olson S. Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization. [Internet] [Masters thesis]. University of New Mexico; 2019. [cited 2019 Dec 07]. Available from: https://digitalrepository.unm.edu/me_etds/165.

Council of Science Editors:

Olson S. Gaussian Process Regression applied to Marine Energy Turbulent Source Tuning via Metamodel Machine Learning Optimization. [Masters Thesis]. University of New Mexico; 2019. Available from: https://digitalrepository.unm.edu/me_etds/165

Linköping University

10. Herwin, Eric. Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer.

Degree: The Division of Statistics and Machine Learning, 2019, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158182

► Achieving optimal efficiency of production in the industrial sector is a process that is continuously under development. In several industrial installations separators, produced by… (more)

Subjects/Keywords: Industrial process; Optimization; Gaussian Process; Deep Kernel Learning; Basin-hopping; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Herwin, E. (2019). Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158182

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Herwin, Eric. “Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer.” 2019. Thesis, Linköping University. Accessed December 07, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158182.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Herwin, Eric. “Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer.” 2019. Web. 07 Dec 2019.

Vancouver:

Herwin E. Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer. [Internet] [Thesis]. Linköping University; 2019. [cited 2019 Dec 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158182.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Herwin E. Optimizing process parameters to increase the quality of the output in a separator : An application of Deep Kernel Learning in combination with the Basin-hopping optimizer. [Thesis]. Linköping University; 2019. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158182

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

11. Benassi, Romain. Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle. : New Bayesian optimization algorithm using a sequential Monte-Carlo approach.

Degree: Docteur es, Traitement du Signal (STIC), 2013, Supélec

URL: http://www.theses.fr/2013SUPL0011

►

Ce travail de thèse s'intéresse au problème de l'optimisation globale d'une fonction coûteuse dans un cadre bayésien. Nous disons qu'une fonction est coûteuse lorsque son… (more)

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Ce travail de thèse s'intéresse au problème de l'optimisation globale d'une fonction coûteuse dans un cadre bayésien. Nous disons qu'une fonction est coûteuse lorsque son évaluation nécessite l’utilisation de ressources importantes (simulations numériques très longues, notamment). Dans ce contexte, il est important d'utiliser des algorithmes d'optimisation utilisant un faible nombre d'évaluations de cette dernière. Nous considérons ici une approche bayésienne consistant à affecter à la fonction à optimiser un a priori sous la forme d'un processus aléatoire gaussien, ce qui permet ensuite de choisir les points d'évaluation de la fonction en maximisant un critère probabiliste indiquant, conditionnellement aux évaluations précédentes, les zones les plus intéressantes du domaine de recherche de l'optimum. Deux difficultés dans le cadre de cette approche peuvent être identifiées : le choix de la valeur des paramètres du processus gaussien et la maximisation efficace du critère. La première difficulté est généralement résolue en substituant aux paramètres l'estimateur du maximum de vraisemblance, ce qui est une méthode peu robuste à laquelle nous préférons une approche dite complètement bayésienne. La contribution de cette thèse est de présenter un nouvel algorithme d'optimisation bayésien, maximisant à chaque étape le critère dit de l'espérance de l'amélioration, et apportant une réponse conjointe aux deux difficultés énoncées à l'aide d'une approche Sequential Monte Carlo. Des résultats numériques, obtenus à partir de cas tests et d'applications industrielles, montrent que les performances de notre algorithme sont bonnes par rapport à celles d’algorithmes concurrents.

This thesis deals with the problem of global optimization of expensive-to-evaluate functions in a Bayesian framework. We say that a function is expensive-to-evaluate when its evaluation requires a significant amount of resources (e.g., very long numerical simulations).In this context, it is important to use optimization algorithms that can deal with a limited number of function evaluations. We consider here a Bayesian approach which consists in assigning a prior to the function, under the form of a Gaussian random process. The idea is then to choose the next evaluation points using a probabilistic criterion that indicates, conditional on the previous evaluations, the most interesting regions of the research domain for the optimizer. Two difficulties in this approach can be identified: the choice of the Gaussian process prior and the maximization of the criterion. The first problem is usually solved by using a maximum likelihood approach, which turns out to be a poorly robust method, and to which we prefer a fully Bayesian approach. The contribution of this work is the introduction of a new Bayesian optimization algorithm, which maximizes the Expected Improvement (EI) criterion, and provides an answer to both problems thanks to a Sequential Monte Carlo approach. Numerical results on benchmark tests show good performances of our algorithm compared to those…

Advisors/Committee Members: Vazquez, Emmanuel (thesis director).

Subjects/Keywords: Optimisation; Processus gaussien; Krigeage; Critère EI; Méthodes SMC; Optimization; Gaussian process; Kriging,; Expected Improvement criterion; SMC methods; 378.242

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Benassi, R. (2013). Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle. : New Bayesian optimization algorithm using a sequential Monte-Carlo approach. (Doctoral Dissertation). Supélec. Retrieved from http://www.theses.fr/2013SUPL0011

Chicago Manual of Style (16^th Edition):

Benassi, Romain. “Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle. : New Bayesian optimization algorithm using a sequential Monte-Carlo approach.” 2013. Doctoral Dissertation, Supélec. Accessed December 07, 2019. http://www.theses.fr/2013SUPL0011.

MLA Handbook (7^th Edition):

Benassi, Romain. “Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle. : New Bayesian optimization algorithm using a sequential Monte-Carlo approach.” 2013. Web. 07 Dec 2019.

Vancouver:

Benassi R. Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle. : New Bayesian optimization algorithm using a sequential Monte-Carlo approach. [Internet] [Doctoral dissertation]. Supélec; 2013. [cited 2019 Dec 07]. Available from: http://www.theses.fr/2013SUPL0011.

Council of Science Editors:

Benassi R. Nouvel algorithme d'optimisation bayésien utilisant une approche Monte-Carlo séquentielle. : New Bayesian optimization algorithm using a sequential Monte-Carlo approach. [Doctoral Dissertation]. Supélec; 2013. Available from: http://www.theses.fr/2013SUPL0011

12. OUYANG RUOFEI. EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS.

Degree: 2016, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/132189

Subjects/Keywords: Multi-Agent System; Machine Learning; Gaussian process; Bayesian Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

RUOFEI, O. (2016). EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/132189

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

RUOFEI, OUYANG. “EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS.” 2016. Thesis, National University of Singapore. Accessed December 07, 2019. http://scholarbank.nus.edu.sg/handle/10635/132189.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

RUOFEI, OUYANG. “EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS.” 2016. Web. 07 Dec 2019.

Vancouver:

RUOFEI O. EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS. [Internet] [Thesis]. National University of Singapore; 2016. [cited 2019 Dec 07]. Available from: http://scholarbank.nus.edu.sg/handle/10635/132189.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

RUOFEI O. EXPLOITING DECENTRALIZED MULTI-AGENT COORDINATION FOR LARGE-SCALE MACHINE LEARNING PROBLEMS. [Thesis]. National University of Singapore; 2016. Available from: http://scholarbank.nus.edu.sg/handle/10635/132189

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

13. Park, Jangho. Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method.

Degree: PhD, Aerospace Engineering, 2019, Virginia Tech

URL: http://hdl.handle.net/10919/91911

► In recent years, as the cost of aircraft design is growing rapidly, and aviation industry is interested in saving time and cost for the design,… (more)

▼ In recent years, as the cost of aircraft design is growing rapidly, and aviation industry is interested in saving time and cost for the design, an accurate design result during the early design stages is particularly important to reduce overall life cycle cost. The purpose of the work to reducing the design cost at the early design stage with design accuracy as high as that of the detailed design. The method of an efficient global optimization (EGO) with variable-fidelity analysis and multidisciplinary design is proposed. Using the variable-fidelity analysis for the function evaluation, high fidelity function evaluations can be replaced by low-fidelity analyses of equivalent accuracy, which leads to considerable cost reduction. As the aircraft system has sub-disciplines coupled by multiple physics, including aerodynamics, structures, and thermodynamics, the accuracy of an individual discipline affects that of all others, and thus the design accuracy during in the early design states. Four distinctive design methods are developed and implemented into the standard Efficient Global Optimization (EGO) framework: 1) the variable-fidelity analysis based on error approximation and calibration of low-fidelity samples, 2) dynamic sampling criteria for both filtering and infilling samples, 3) a dynamic fidelity indicator (DFI) for the selection of analysis fidelity for infilled samples, and 4) Multi-response Kriging model with an iterative Maximum Likelihood estimation (iMLE). The methods are validated with analytic functions, and the improvement in cost efficiency through the overall design process is observed, while maintaining the design accuracy, by a comparison with existing design methods. For the practical applications, the methods are applied to the design optimization of airfoil and complete aircraft configuration, respectively. The design results are compared with those by existing methods, and it is found the method results design results of accuracies equivalent to or higher than high-fidelity analysis-alone design at cost reduced by orders of magnitude. Advisors/Committee Members: Choi, Seongim Sarah (committeechair), Raj, Pradeep (committeechair), Chen, Xi (committee member), Devenport, William J. (committee member).

Subjects/Keywords: Efficient Global Optimization(EGO); Variable-Fidelity(VF) Analysis; Data mining; Gaussian Process Regression(GPR) modeling; Design of Experiment(DoE)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Park, J. (2019). Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/91911

Chicago Manual of Style (16^th Edition):

Park, Jangho. “Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method.” 2019. Doctoral Dissertation, Virginia Tech. Accessed December 07, 2019. http://hdl.handle.net/10919/91911.

MLA Handbook (7^th Edition):

Park, Jangho. “Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method.” 2019. Web. 07 Dec 2019.

Vancouver:

Park J. Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10919/91911.

Council of Science Editors:

Park J. Efficient Global Optimization of Multidisciplinary System using Variable Fidelity Analysis and Dynamic Sampling Method. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/91911

University of Waterloo

14. Ali Yusuf, Yusuf. Green Petroleum Refining - Mathematical Models for Optimizing Petroleum Refining Under Emission Constraints.

Degree: 2013, University of Waterloo

URL: http://hdl.handle.net/10012/7860

► Petroleum refining processes provide the daily requirements of energy for the global market. Each refining process produces wastes that have the capacity to harm the… (more)

Subjects/Keywords: Optimization Petroleum Refining; Process Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ali Yusuf, Y. (2013). Green Petroleum Refining - Mathematical Models for Optimizing Petroleum Refining Under Emission Constraints. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7860

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ali Yusuf, Yusuf. “Green Petroleum Refining - Mathematical Models for Optimizing Petroleum Refining Under Emission Constraints.” 2013. Thesis, University of Waterloo. Accessed December 07, 2019. http://hdl.handle.net/10012/7860.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ali Yusuf, Yusuf. “Green Petroleum Refining - Mathematical Models for Optimizing Petroleum Refining Under Emission Constraints.” 2013. Web. 07 Dec 2019.

Vancouver:

Ali Yusuf Y. Green Petroleum Refining - Mathematical Models for Optimizing Petroleum Refining Under Emission Constraints. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10012/7860.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ali Yusuf Y. Green Petroleum Refining - Mathematical Models for Optimizing Petroleum Refining Under Emission Constraints. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7860

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

15. Sacher, Matthieu. Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition : Advanced surrogate-based optimization methods - Application to racing yachts performance.

Degree: Docteur es, Mécanique-matériaux, 2018, Paris, ENSAM

URL: http://www.theses.fr/2018ENAM0032

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L’optimisation de la performance des voiliers est un problème difficile en raison de la complexité du systèmemécanique (couplage aéro-élastique et hydrodynamique) et du nombre important… (more)

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L’optimisation de la performance des voiliers est un problème difficile en raison de la complexité du systèmemécanique (couplage aéro-élastique et hydrodynamique) et du nombre important de paramètres à optimiser (voiles, gréement,etc.). Malgré le fait que l’optimisation des voiliers est empirique dans la plupart des cas aujourd’hui, les approchesnumériques peuvent maintenant devenir envisageables grâce aux dernières améliorations des modèles physiques et despuissances de calcul. Les calculs aéro-hydrodynamiques restent cependant très coûteux car chaque évaluation demandegénéralement la résolution d’un problème non linéaire d’interaction fluide-structure. Ainsi, l’objectif central de cette thèseest de proposer et développer des méthodes originales dans le but de minimiser le coût numérique de l’optimisation dela performance des voiliers. L’optimisation globale par méta-modèles Gaussiens est utilisée pour résoudre différents problèmesd’optimisation. La méthode d’optimisation par méta-modèles est étendue aux cas d’optimisations sous contraintes,incluant de possibles points non évaluables, par une approche de type classification. L’utilisation de méta-modèles à fidélitésmultiples est également adaptée à la méthode d’optimisation globale. Les applications concernent des problèmesd’optimisation originaux où la performance est modélisée expérimentalement et/ou numériquement. Ces différentes applicationspermettent de valider les développements des méthodes d’optimisation sur des cas concrets et complexes, incluantdes phénomènes d’interaction fluide-structure.

Sailing yacht performance optimization is a difficult problem due to the high complexity of the mechanicalsystem (aero-elastic and hydrodynamic coupling) and the large number of parameters to optimize (sails, rigs, etc.).Despite the fact that sailboats optimization is empirical in most cases today, the numerical optimization approach is nowconsidered as possible because of the latest advances in physical models and computing power. However, these numericaloptimizations remain very expensive as each simulation usually requires solving a non-linear fluid-structure interactionproblem. Thus, the central objective of this thesis is to propose and to develop original methods aiming at minimizing thenumerical cost of sailing yacht performance optimization. The Efficient Global Optimization (EGO) is therefore appliedto solve various optimization problems. The original EGO method is extended to cases of optimization under constraints,including possible non computable points, using a classification-based approach. The use of multi-fidelity surrogates isalso adapted to the EGO method. The applications treated in this thesis concern the original optimization problems inwhich the performance is modeled experimentally and/or numerically. These various applications allow for the validationof the developments in optimization methods on real and complex problems, including fluid-structure interactionphenomena.

Advisors/Committee Members: Astolfi, Jacques André (thesis director), Le Maître, Olivier P. (thesis director).

Subjects/Keywords: Optimisation globale; Méta-Modèles; Processus Gaussien; Classification; Multi-Fidélité; Interaction fluide-Structure; Global optimization; Surrogate models; Gaussian process; Classification; Multi-Fidelity; Fluid-Structure interaction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sacher, M. (2018). Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition : Advanced surrogate-based optimization methods - Application to racing yachts performance. (Doctoral Dissertation). Paris, ENSAM. Retrieved from http://www.theses.fr/2018ENAM0032

Chicago Manual of Style (16^th Edition):

Sacher, Matthieu. “Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition : Advanced surrogate-based optimization methods - Application to racing yachts performance.” 2018. Doctoral Dissertation, Paris, ENSAM. Accessed December 07, 2019. http://www.theses.fr/2018ENAM0032.

MLA Handbook (7^th Edition):

Sacher, Matthieu. “Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition : Advanced surrogate-based optimization methods - Application to racing yachts performance.” 2018. Web. 07 Dec 2019.

Vancouver:

Sacher M. Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition : Advanced surrogate-based optimization methods - Application to racing yachts performance. [Internet] [Doctoral dissertation]. Paris, ENSAM; 2018. [cited 2019 Dec 07]. Available from: http://www.theses.fr/2018ENAM0032.

Council of Science Editors:

Sacher M. Méthodes avancées d'optimisation par méta-modèles – Applicationà la performance des voiliers de compétition : Advanced surrogate-based optimization methods - Application to racing yachts performance. [Doctoral Dissertation]. Paris, ENSAM; 2018. Available from: http://www.theses.fr/2018ENAM0032

Cal Poly

16. Baukol, Collin R. Development of an Integrated Gaussian Process Metamodeling Application for Engineering Design.

Degree: MS, Aerospace Engineering, 2009, Cal Poly

URL: https://digitalcommons.calpoly.edu/theses/115 ; 10.15368/theses.2009.71

► As engineering technologies continue to grow and improve, the complexities in the engineering models which utilize these technologies also increase. This seemingly endless cycle of… (more)

Subjects/Keywords: Metamodeling; Gaussian Process; Application; Metamodel; Systems Engineering and Multidisciplinary Design Optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Baukol, C. R. (2009). Development of an Integrated Gaussian Process Metamodeling Application for Engineering Design. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/115 ; 10.15368/theses.2009.71

Chicago Manual of Style (16^th Edition):

Baukol, Collin R. “Development of an Integrated Gaussian Process Metamodeling Application for Engineering Design.” 2009. Masters Thesis, Cal Poly. Accessed December 07, 2019. https://digitalcommons.calpoly.edu/theses/115 ; 10.15368/theses.2009.71.

MLA Handbook (7^th Edition):

Baukol, Collin R. “Development of an Integrated Gaussian Process Metamodeling Application for Engineering Design.” 2009. Web. 07 Dec 2019.

Vancouver:

Baukol CR. Development of an Integrated Gaussian Process Metamodeling Application for Engineering Design. [Internet] [Masters thesis]. Cal Poly; 2009. [cited 2019 Dec 07]. Available from: https://digitalcommons.calpoly.edu/theses/115 ; 10.15368/theses.2009.71.

Council of Science Editors:

Baukol CR. Development of an Integrated Gaussian Process Metamodeling Application for Engineering Design. [Masters Thesis]. Cal Poly; 2009. Available from: https://digitalcommons.calpoly.edu/theses/115 ; 10.15368/theses.2009.71

17. Ploé, Patrick. Surrogate-based optimization of hydrofoil shapes using RANS simulations : Optimisation de géométries d’hydrofoils par modèles de substitution construits à partir de simulations RANS.

Degree: Docteur es, Mécanique des Milieux Fluides, 2018, Ecole centrale de Nantes

URL: http://www.theses.fr/2018ECDN0012

►

Cette thèse présente un framework d’optimisation pour la conception hydrodynamique de forme d’hydrofoils. L’optimisation d’hydrofoil par simulation implique des objectifs d’optimisation divergents et impose des… (more)

▼

Cette thèse présente un framework d’optimisation pour la conception hydrodynamique de forme d’hydrofoils. L’optimisation d’hydrofoil par simulation implique des objectifs d’optimisation divergents et impose des compromis contraignants en raison du coût des simulations numériques et des budgets limités généralement alloués à la conception des navires. Le framework fait appel à l’échantillonnage séquentiel et aux modèles de substitution. Un modèle prédictif est construit en utilisant la Régression par Processus Gaussien (RPG) à partir des données issues de simulations fluides effectuées sur différentes géométries d’hydrofoils. Le modèle est ensuite combiné à d’autres critères dans une fonction d’acquisition qui est évaluée sur l’espace de conception afin de définir une nouvelle géométrie qui est testée et dont les paramètres et la réponse sont ajoutés au jeu de données, améliorant ainsi le modèle. Une nouvelle fonction d’acquisition a été développée, basée sur la variance RPG et la validation croisée des données. Un modeleur géométrique a également été développé afin de créer automatiquement les géométries d’hydrofoil a partir des paramètres déterminés par l’optimiseur. Pour compléter la boucle d’optimisation,FINE/Marine, un solveur fluide RANS, a été intégré dans le framework pour exécuter les simulations fluides. Les capacités d’optimisation ont été testées sur des cas tests analytiques montrant que la nouvelle fonction d’acquisition offre plus de robustesse que d’autres fonctions d’acquisition existantes. L’ensemble du framework a ensuite été testé sur des optimisations de sections 2Dd’hydrofoil ainsi que d’hydrofoil 3D avec surface libre. Dans les deux cas, le processus d’optimisation fonctionne, permettant d’optimiser les géométries d’hydrofoils et confirmant les performances obtenues sur les cas test analytiques. Les optima semblent cependant être assez sensibles aux conditions opérationnelles.

This thesis presents a practical hydrodynamic optimization framework for hydrofoil shape design. Automated simulation based optimization of hydrofoil is a challenging process. It may involve conflicting optimization objectives, but also impose a trade-off between the cost of numerical simulations and the limited budgets available for ship design. The optimization frameworkis based on sequential sampling and surrogate modeling. Gaussian Process Regression (GPR) is used to build a predictive model based on data issued from fluid simulations of selected hydrofoil geometries. The GPR model is then combined with other criteria into an acquisition function that isevaluated over the design space, to define new querypoints that are added to the data set in order to improve the model. A custom acquisition function is developed, based on GPR variance and cross validation of the data.A hydrofoil geometric modeler is also developed to automatically create the hydrofoil shapes based on the parameters determined by the optimizer. To complete the optimization loop, FINE/Marine, a RANS flow solver, is embedded into the framework to…

Advisors/Committee Members: Visonneau, Michel (thesis director).

Subjects/Keywords: Optimisation par modèle de substitution; Régression par processus gaussien; Simulations RANS; Modeleur géométrique; Hydrofoils; Architecture navale; Surrogate-based optimization; Gaussian process regression; RANS simulations; Geometric modeling; Hydrofoils; Ship design

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ploé, P. (2018). Surrogate-based optimization of hydrofoil shapes using RANS simulations : Optimisation de géométries d’hydrofoils par modèles de substitution construits à partir de simulations RANS. (Doctoral Dissertation). Ecole centrale de Nantes. Retrieved from http://www.theses.fr/2018ECDN0012

Chicago Manual of Style (16^th Edition):

Ploé, Patrick. “Surrogate-based optimization of hydrofoil shapes using RANS simulations : Optimisation de géométries d’hydrofoils par modèles de substitution construits à partir de simulations RANS.” 2018. Doctoral Dissertation, Ecole centrale de Nantes. Accessed December 07, 2019. http://www.theses.fr/2018ECDN0012.

MLA Handbook (7^th Edition):

Ploé, Patrick. “Surrogate-based optimization of hydrofoil shapes using RANS simulations : Optimisation de géométries d’hydrofoils par modèles de substitution construits à partir de simulations RANS.” 2018. Web. 07 Dec 2019.

Vancouver:

Ploé P. Surrogate-based optimization of hydrofoil shapes using RANS simulations : Optimisation de géométries d’hydrofoils par modèles de substitution construits à partir de simulations RANS. [Internet] [Doctoral dissertation]. Ecole centrale de Nantes; 2018. [cited 2019 Dec 07]. Available from: http://www.theses.fr/2018ECDN0012.

Council of Science Editors:

Ploé P. Surrogate-based optimization of hydrofoil shapes using RANS simulations : Optimisation de géométries d’hydrofoils par modèles de substitution construits à partir de simulations RANS. [Doctoral Dissertation]. Ecole centrale de Nantes; 2018. Available from: http://www.theses.fr/2018ECDN0012

18. Muthu selvan N B. Application of gaussian and cauchy inspired pso algorithms for power system optimization problems;.

Degree: Gaussian and cauchy inspired pso algorithms for power system optimization, 2014, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/16451

►

The main objective of this research work is to develop an enhanced newlineParticle Swarm Optimization (PSO) algorithm for solving various power newlinesystem generation scheduling problems.… (more)

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The main objective of this research work is to develop an enhanced newlineParticle Swarm Optimization (PSO) algorithm for solving various power newlinesystem generation scheduling problems. The enhanced PSO algorithm is newlinedeveloped from the conventional PSO algorithm by the combined application newlineof Gaussian and Cauchy distribution and hence it is appropriately termed as newlineGaussian Cauchy inspired Particle Swarm Optimization (GCPSO). The newlinemodifications made into the conventional PSO algorithm ensure more reliable newlineand faster convergence in obtaining a global optimal solution. The integrity of newlinethe proposed algorithm lies in the significance of dealing the optimization newlineproblems without placing any restrictions on the structure or type of the newlinefunction to be optimized. The various power system problems that are solved newlineusing GCPSO algorithm include: Economic Dispatch (ED), Economic newlineEmission Load Dispatch (EELD), Multi Constrained Economic Dispatch newline(MCED) with non-smooth fuel cost function, DC Optimal Power Flow newline(DCOPF), AC Optimal Power Flow (ACOPF), Security Constrained Optimal newlinePower Flow (SCOPF), Transient Stability Constrained OPF (TSCOPF), newlineOptimal Power Flow with Flexible AC Transmission System (FACTS) newlinecontrollers and wind farm integrated ED and OPF problems. Initially the ED problem is solved with the PSO algorithm and the newlinemajor shortcomings of the PSO algorithm are analyzed. The analysis reveals newlinecertain major limitations such as relatively large computational time, tendency newlinetowards premature convergence and search inconsistency. Hence there is a newlinenecessity to enhance the PSO algorithm. The feasible modifications using newlinevarious probability distributions that can be introduced into the PSO newlinealgorithm are investigated. From this investigation it is found that the newlineapplication of Gaussian and Cauchy distributions into the velocity update newlineequation are appropriate for enhancing the PSO algorithm.

Appendix p.147-161, References p.162-169.

Advisors/Committee Members: Somasundaram P.

Subjects/Keywords: Cauchy; Electrical engineering; Gaussian; Power system optimization

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APA (6^th Edition):

B, M. s. N. (2014). Application of gaussian and cauchy inspired pso algorithms for power system optimization problems;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/16451

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

B, Muthu selvan N. “Application of gaussian and cauchy inspired pso algorithms for power system optimization problems;.” 2014. Thesis, Anna University. Accessed December 07, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/16451.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

B, Muthu selvan N. “Application of gaussian and cauchy inspired pso algorithms for power system optimization problems;.” 2014. Web. 07 Dec 2019.

Vancouver:

B MsN. Application of gaussian and cauchy inspired pso algorithms for power system optimization problems;. [Internet] [Thesis]. Anna University; 2014. [cited 2019 Dec 07]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/16451.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

B MsN. Application of gaussian and cauchy inspired pso algorithms for power system optimization problems;. [Thesis]. Anna University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/16451

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Manchester

19. Phillips, Nick. Modelling and analysis of oscillations in gene expression through neural development.

Degree: 2016, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629

►

The timing of differentiation underlies the development of any organ system. In neural development, the expression of the transcription factor Hes1 has been shown to… (more)

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The timing of differentiation underlies the development of any organ system. In neural development, the expression of the transcription factor Hes1 has been shown to be oscillatory in neural progenitors, but at a low steady state in differentiated neurons. This change in the dynamics of expression marks the timing of differentiation. We previously constructed a mathematical model to test the experimental hypothesis that the topology of the miR-9/Hes1 network and specifically the accumulation of the micro-RNA, miR-9, could terminate Hes1 oscillations and account for the timing of neuronal differentiation, using deterministic delay differential equations.However, biochemical reactions are the result of random encounters between discrete numbers of molecules, and some of these molecules may be present at low numbers. The finite number of molecules interacting within the system leads to inherent randomness, and this is known as intrinsic stochasticity. The stochastic model predicts that low molecular number causes the time to differentiation to be distributed, which is in agreement with recent experimental evidence and considered important to generate cell type diversity. For the exact same model, fewer reacting molecules causes a decrease in the average time to differentiation, showing that the number of molecules can systematically change the timing of differentiation.Oscillations are important for a wide range of biological processes, but current methods for discovering oscillatory genes have primarily been designed for measurements performed on a population of cells. We introduce a new approach for analysing biological time series data designed for cases where the underlying dynamics of gene expression is inherently noisy at a single cell level. Our analysis method combines mechanistic stochastic modelling with the powerful methods of Bayesian nonparametric regression, and can distinguish oscillatory expression in single cell data from random fluctuations of nonoscillatory gene expression, despite peak-to-peak variability in period and amplitude of single cell oscillations.Models of gene expression commonly involve delayed biological processes, but the combination of stochasticity, delay and nonlinearity lead to emergent dynamics that are not understood at a theoretical level. We develop a theory to explain these effects, and apply it to a simple model of gene regulation. The new theory can account for long time-scale dynamics and nonlinear character of the system that emerge when the number of interacting molecules becomes low. Both the absolute length and the uncertainty in the delay time are shown to be crucial in controlling the magnitude of nonlinear effects.

During neural development stem cells produce neurons through the process of differentiation. During differentiation, the expression of key genes controlling the stem cell state change from oscillatory to a low steady state. We use mathematical modelling to understand how the dynamics of this transition is regulated, and how the randomness caused by few…

Advisors/Committee Members: RATTRAY, MAGNUS M, Rattray, Magnus, Papalopulu, Nancy.

Subjects/Keywords: neural stem cells; stochasticity; gaussian process; differentiation

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APA (6^th Edition):

Phillips, N. (2016). Modelling and analysis of oscillations in gene expression through neural development. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629

Chicago Manual of Style (16^th Edition):

Phillips, Nick. “Modelling and analysis of oscillations in gene expression through neural development.” 2016. Doctoral Dissertation, University of Manchester. Accessed December 07, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629.

MLA Handbook (7^th Edition):

Phillips, Nick. “Modelling and analysis of oscillations in gene expression through neural development.” 2016. Web. 07 Dec 2019.

Vancouver:

Phillips N. Modelling and analysis of oscillations in gene expression through neural development. [Internet] [Doctoral dissertation]. University of Manchester; 2016. [cited 2019 Dec 07]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629.

Council of Science Editors:

Phillips N. Modelling and analysis of oscillations in gene expression through neural development. [Doctoral Dissertation]. University of Manchester; 2016. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:299629

University of Sydney

20. Marchant Matus, Roman. Bayesian Optimisation for Planning in Dynamic Environments .

Degree: 2015, University of Sydney

URL: http://hdl.handle.net/2123/14497

► This thesis addresses the problem of trajectory planning for monitoring extreme values of an environmental phenomenon that changes in space and time. The most relevant… (more)

▼ This thesis addresses the problem of trajectory planning for monitoring extreme values of an environmental phenomenon that changes in space and time. The most relevant case study corresponds to environmental monitoring using an autonomous mobile robot for air, water and land pollution monitoring. Since the dynamics of the phenomenon are initially unknown, the planning algorithm needs to satisfy two objectives simultaneously: 1) Learn and predict spatial-temporal patterns and, 2) find areas of interest (e.g. high pollution), addressing the exploration-exploitation trade-off. Consequently, the thesis brings the following contributions: Firstly, it applies and formulates Bayesian Optimisation (BO) to planning in robotics. By maintaining a Gaussian Process (GP) model of the environmental phenomenon the planning algorithms are able to learn the spatial and temporal patterns. A new family of acquisition functions which consider the position of the robot is proposed, allowing an efficient trajectory planning. Secondly, BO is generalised for optimisation over continuous paths, not only determining where and when to sample, but also how to get there. Under these new circumstances, the optimisation of the acquisition function for each iteration of the BO algorithm becomes costly, thus a second layer of BO is included in order to effectively reduce the number of iterations. Finally, this thesis presents Sequential Bayesian Optimisation (SBO), which is a generalisation of the plain BO algorithm with the goal of achieving non-myopic trajectory planning. SBO is formulated under a Partially Observable Markov Decision Process (POMDP) framework, which can find the optimal decision for a sequence of actions with their respective outcomes. An online solution of the POMDP based on Monte Carlo Tree Search (MCTS) allows an efficient search of the optimal action for multistep lookahead. The proposed planning algorithms are evaluated under different scenarios. Experiments on large scale ozone pollution monitoring and indoor light intensity monitoring are conducted for simulated and real robots. The results show the advantages of planning over continuous paths and also demonstrate the benefit of deeper search strategies using SBO.

Subjects/Keywords: Bayesian; Optimisation; Planning; Robotics; POMDP; Gaussian-Process

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Marchant Matus, R. (2015). Bayesian Optimisation for Planning in Dynamic Environments . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14497

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Marchant Matus, Roman. “Bayesian Optimisation for Planning in Dynamic Environments .” 2015. Thesis, University of Sydney. Accessed December 07, 2019. http://hdl.handle.net/2123/14497.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Marchant Matus, Roman. “Bayesian Optimisation for Planning in Dynamic Environments .” 2015. Web. 07 Dec 2019.

Vancouver:

Marchant Matus R. Bayesian Optimisation for Planning in Dynamic Environments . [Internet] [Thesis]. University of Sydney; 2015. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/2123/14497.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Marchant Matus R. Bayesian Optimisation for Planning in Dynamic Environments . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/14497

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Sydney

21. Wilson, Troy Daniel. Adaptive Sampling For Efficient Online Modelling .

Degree: 2017, University of Sydney

URL: http://hdl.handle.net/2123/17257

► This thesis examines methods enabling autonomous systems to make active sampling and planning decisions in real time. Gaussian Process (GP) regression is chosen as a… (more)

Subjects/Keywords: Planning; Entropy; Gaussian Process; Heteroscedastic; Autonomous

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APA (6^th Edition):

Wilson, T. D. (2017). Adaptive Sampling For Efficient Online Modelling . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17257

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wilson, Troy Daniel. “Adaptive Sampling For Efficient Online Modelling .” 2017. Thesis, University of Sydney. Accessed December 07, 2019. http://hdl.handle.net/2123/17257.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wilson, Troy Daniel. “Adaptive Sampling For Efficient Online Modelling .” 2017. Web. 07 Dec 2019.

Vancouver:

Wilson TD. Adaptive Sampling For Efficient Online Modelling . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/2123/17257.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wilson TD. Adaptive Sampling For Efficient Online Modelling . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17257

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Leiden University

22. Wang, H. Stochastic and deterministic algorithms for continuous black-box optimization.

Degree: 2018, Leiden University

URL: http://hdl.handle.net/1887/66671

► Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous… (more)

Subjects/Keywords: Stochastic optimization; Acquisition function; Orthogonalization; Gaussian process regression; Efficient global optimization; Parallelization; Hypervolume indicator; Stochastic optimization; Acquisition function; Orthogonalization; Gaussian process regression; Efficient global optimization; Parallelization; Hypervolume indicator

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, H. (2018). Stochastic and deterministic algorithms for continuous black-box optimization. (Doctoral Dissertation). Leiden University. Retrieved from http://hdl.handle.net/1887/66671

Chicago Manual of Style (16^th Edition):

Wang, H. “Stochastic and deterministic algorithms for continuous black-box optimization.” 2018. Doctoral Dissertation, Leiden University. Accessed December 07, 2019. http://hdl.handle.net/1887/66671.

MLA Handbook (7^th Edition):

Wang, H. “Stochastic and deterministic algorithms for continuous black-box optimization.” 2018. Web. 07 Dec 2019.

Vancouver:

Wang H. Stochastic and deterministic algorithms for continuous black-box optimization. [Internet] [Doctoral dissertation]. Leiden University; 2018. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/1887/66671.

Council of Science Editors:

Wang H. Stochastic and deterministic algorithms for continuous black-box optimization. [Doctoral Dissertation]. Leiden University; 2018. Available from: http://hdl.handle.net/1887/66671

23. Rousis, Damon. A pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives.

Degree: PhD, Aerospace Engineering, 2011, Georgia Tech

URL: http://hdl.handle.net/1853/41136

► The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of… (more)

▼ The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of commercial aircraft. As the number of technology alternatives grows along with model complexity, current methods for Pareto finding and multiobjective optimization quickly become computationally infeasible. Coupled with the large uncertainty in the early stages of design, optimal designs are sought while avoiding the computational burden of excessive function calls when a single design change or technology assumption could alter the results. This motivates the need for a robust and efficient evaluation methodology for quantitative assessment of competing concepts. This research presents a novel approach that combines Bayesian adaptive sampling with surrogate-based optimization to efficiently place designs near Pareto frontier intersections of competing concepts. Efficiency is increased over sequential multiobjective optimization by focusing computational resources specifically on the location in the design space where optimality shifts between concepts. At the intersection of Pareto frontiers, the selection decisions are most sensitive to preferences place on the objectives, and small perturbations can lead to vastly different final designs. These concepts are incorporated into an evaluation methodology that ultimately reduces the number of failed cases, infeasible designs, and Pareto dominated solutions across all concepts. A set of algebraic samples along with a truss design problem are presented as canonical examples for the proposed approach. The methodology is applied to the design of ultra-high bypass ratio turbofans to guide NASA's technology development efforts for future aircraft. Geared-drive and variable geometry bypass nozzle concepts are explored as enablers for increased bypass ratio and potential alternatives over traditional configurations. The method is shown to improve sampling efficiency and provide clusters of feasible designs that motivate a shift towards revolutionary technologies that reduce fuel burn, emissions, and noise on future aircraft. Advisors/Committee Members: Mavris, Dimitri (Committee Chair), Berton, Jeff (Committee Member), German, Brian (Committee Member), Jagoda, Jeff (Committee Member), Volovoi, Vitali (Committee Member).

Subjects/Keywords: Kriging; Expected improvement; S-Pareto; Gaussian process; Stochastic processes; Gaussian processes; Multidisciplinary design optimization; Combinatorial optimization; Monte Carlo method

…Optimization . . . . . . . . . . . . . . 11 6 Graphical Interpretation of Weighted Decision… …23 8 Example Decomposition for Analytical Hierarchy Process . . . . . . . 26 9… …Morphological Matrix . . . . . . . . . . . 32 12 Sequential Optimization of Three Competing… …34 14 Simultaneous Multiobjective Optimization . . . . . . . . . . . . . . . 36 15… …Concept Comparison with Ordinal Optimization . . . . . . . . . . . . 42 16 Fast Probability…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rousis, D. (2011). A pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/41136

Chicago Manual of Style (16^th Edition):

Rousis, Damon. “A pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives.” 2011. Doctoral Dissertation, Georgia Tech. Accessed December 07, 2019. http://hdl.handle.net/1853/41136.

MLA Handbook (7^th Edition):

Rousis, Damon. “A pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives.” 2011. Web. 07 Dec 2019.

Vancouver:

Rousis D. A pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/1853/41136.

Council of Science Editors:

Rousis D. A pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/41136

Duke University

24. Wei, Hongchuan. Sensor Planning for Bayesian Nonparametric Target Modeling .

Degree: 2016, Duke University

URL: http://hdl.handle.net/10161/12863

► Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including… (more)

▼ Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns. Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time. Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean… Advisors/Committee Members: Ferrari, Silvia (advisor), Zavlanos, Michael M (advisor).

Subjects/Keywords: Mechanical engineering; Bayesian nonparametric; Dirichlet process; Gaussian process; sensor planning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wei, H. (2016). Sensor Planning for Bayesian Nonparametric Target Modeling . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/12863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wei, Hongchuan. “Sensor Planning for Bayesian Nonparametric Target Modeling .” 2016. Thesis, Duke University. Accessed December 07, 2019. http://hdl.handle.net/10161/12863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wei, Hongchuan. “Sensor Planning for Bayesian Nonparametric Target Modeling .” 2016. Web. 07 Dec 2019.

Vancouver:

Wei H. Sensor Planning for Bayesian Nonparametric Target Modeling . [Internet] [Thesis]. Duke University; 2016. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/10161/12863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wei H. Sensor Planning for Bayesian Nonparametric Target Modeling . [Thesis]. Duke University; 2016. Available from: http://hdl.handle.net/10161/12863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

25. Veenendaal, G. van. Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm.

Degree: 2015, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/307362

► This paper presents the Tree-GP algorithm: a scalable Bayesian global numerical optimization algorithm. The algorithm focuses on optimizing evaluation functions that are very expensive to… (more)

Subjects/Keywords: Tree-GP; optimization; minimization; numerical; scalable; Gaussian; Gaussian process; regression; Gaussian process regression; Bayesian; Vantage-point; Vantage; Vantage-point tree; tree; mixture model

…9 2.3 2.3.1 Gaussian process regression Gaussian process Our algorithm puts a prior… …distribution over the loss function by modeling it with Gaussian process regression. Gaussian process… …regression is a powerful regression technique that models the loss function with a Gaussian process… …A Gaussian process is formally defined as a probability distribution over functions y(… …from the Gaussian process as m(x) = E[y(x)], k(x, x0 )…

10 more images…

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APA (6^th Edition):

Veenendaal, G. v. (2015). Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/307362

Chicago Manual of Style (16^th Edition):

Veenendaal, G van. “Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm.” 2015. Masters Thesis, Universiteit Utrecht. Accessed December 07, 2019. http://dspace.library.uu.nl:8080/handle/1874/307362.

MLA Handbook (7^th Edition):

Veenendaal, G van. “Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm.” 2015. Web. 07 Dec 2019.

Vancouver:

Veenendaal Gv. Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2019 Dec 07]. Available from: http://dspace.library.uu.nl:8080/handle/1874/307362.

Council of Science Editors:

Veenendaal Gv. Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm. [Masters Thesis]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/307362

Penn State University

26. Chang, Won. Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data.

Degree: PhD, Statistics, 2014, Penn State University

URL: https://etda.libraries.psu.edu/catalog/22487

► This thesis focuses on statistical methods to calibrate complex computer models using high-dimensional spatial data sets. This work is motivated by important research problems in… (more)

▼ This thesis focuses on statistical methods to calibrate complex computer models using high-dimensional spatial data sets. This work is motivated by important research problems in climate science where such computer models are frequently used. Climate models play a central role in generating projections of future climate. An important source of uncertainty about future projections from these models is due to uncertainty about input parameters that are key drivers of the resulting hindcasts and projections. Climate model calibration is a statistical framework for inferring the input parameters by combining information from climate model runs and observational data. When the data are in the form of high-dimensional spatial fields, climate model emulation (approximation) and calibration can pose significant modeling and computational challenges. The goal of this research is to develop new approaches to computer model calibration that are computationally efficient, accurate, and carefully account for uncertainties. The main contributions of this thesis are three-fold: (1) to develop a highly efficient reduced-dimensional climate model calibration approach that enables the use of high-dimensional spatial data; (2) to formulate a novel calibration method based on block composite likelihood and study the asymptotic properties of the resulting estimates for input parameters; and (3) to introduce a calibration framework that generalizes the existing method to the one-dimensional exponential family and formulate a reduced-dimensional approach that can efficiently handle the high-dimensional non-Gaussian spatial data. Our methods provide insights about current and future climate. In our first application we make projections of the North Atlantic Meridional Overturning Circulation (AMOC), an ocean circulation that transports heat from low- to high-latitude areas in the Atlantic and contributes to the mild climate in Northern and Western Europe. AMOC changes are projected to impact human and natural systems. We demonstrate that utilizing information from high-dimensional spatial data reduces parametric uncertainty and thus results in an AMOC projection with reduced uncertainties. In the second case study, we demonstrate an application of our approach for non-Gaussian spatial data to calibration of a Greenland ice sheet model and show that our approach can improve upon current methods for projections of sea level rise contributed by the Greenland ice sheet.

Subjects/Keywords: Climate Model Calibration; Gaussian Process; High-dimensional Spatial Data; Non-Gaussian Spatial Data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chang, W. (2014). Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/22487

Chicago Manual of Style (16^th Edition):

Chang, Won. “Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data.” 2014. Doctoral Dissertation, Penn State University. Accessed December 07, 2019. https://etda.libraries.psu.edu/catalog/22487.

MLA Handbook (7^th Edition):

Chang, Won. “Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data.” 2014. Web. 07 Dec 2019.

Vancouver:

Chang W. Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data. [Internet] [Doctoral dissertation]. Penn State University; 2014. [cited 2019 Dec 07]. Available from: https://etda.libraries.psu.edu/catalog/22487.

Council of Science Editors:

Chang W. Climate Model Calibration Using High-Dimensional and Non-Gaussian Spatial Data. [Doctoral Dissertation]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/22487

Carnegie Mellon University

27. Castellanos, Lucia. Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms.

Degree: 2013, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/273

► The primate hand, a biomechanical structure with over twenty kinematic degrees of freedom, has an elaborate anatomical architecture. Although the hand requires complex, coordinated neural… (more)

▼ The primate hand, a biomechanical structure with over twenty kinematic degrees of freedom, has an elaborate anatomical architecture. Although the hand requires complex, coordinated neural control, it endows its owner with an astonishing range of dexterous finger movements. Despite a century of research, however, the neural mechanisms that enable finger and grasping movements in primates are largely unknown. In this thesis, we investigate statistical models of finger movement that can provide insights into the mechanics of the hand, and that can have applications in neural-motor prostheses, enabling people with limb loss to regain natural function of the hands. There are many challenges associated with (1) the understanding and modeling of the kinematics of fingers, and (2) the mapping of intracortical neural recordings into motor commands that can be used to control a Brain-Machine Interface. These challenges include: potential nonlinearities; confounded sources of variation in experimental datasets; and dealing with high degrees of kinematic freedom. In this work we analyze kinematic and neural datasets from repeated-trial experiments of hand motion, with the following contributions: We identified static, nonlinear, low-dimensional representations of grasping finger motion, with accompanying evidence that these nonlinear representations are better than linear representations at predicting the type of object being grasped over the course of a reach-to-grasp movement. In addition, we show evidence of better encoding of these nonlinear (versus linear) representations in the firing of some neurons collected from the primary motor cortex of rhesus monkeys. A functional alignment of grasping trajectories, based on total kinetic energy, as a strategy to account for temporal variation and to exploit a repeated-trial experiment structure. An interpretable model for extracting dynamic synergies of finger motion, based on Gaussian Processes, that decomposes and reduces the dimensionality of variance in the dataset. We derive efficient algorithms for parameter estimation, show accurate reconstruction of grasping trajectories, and illustrate the interpretation of the model parameters. Sound evidence of single-neuron decoding of interpretable grasping events, plus insights about the amount of grasping information extractable from just a single neuron. The Laplace Gaussian Filter (LGF), a deterministic approximation to the posterior mean that is more accurate than Monte Carlo approximations for the same computational cost, and that in an off-line decoding task is more accurate than the standard Population Vector Algorithm.

Subjects/Keywords: Variance Decomposition; Multivariate Gaussian Process Factor Analysis; Laplace Gaussian Filter; Functional Data Alignment; Encoding; Decoding

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APA (6^th Edition):

Castellanos, L. (2013). Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/273

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Castellanos, Lucia. “Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms.” 2013. Thesis, Carnegie Mellon University. Accessed December 07, 2019. http://repository.cmu.edu/dissertations/273.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Castellanos, Lucia. “Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms.” 2013. Web. 07 Dec 2019.

Vancouver:

Castellanos L. Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms. [Internet] [Thesis]. Carnegie Mellon University; 2013. [cited 2019 Dec 07]. Available from: http://repository.cmu.edu/dissertations/273.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Castellanos L. Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms. [Thesis]. Carnegie Mellon University; 2013. Available from: http://repository.cmu.edu/dissertations/273

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

28. Nie, Yisu. Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications.

Degree: 2014, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/380

► This thesis study focuses on the development of model-based optimization strategies for the integration of process scheduling and dynamic optimization, and applications of the integrated… (more)

▼ This thesis study focuses on the development of model-based optimization strategies for the integration of process scheduling and dynamic optimization, and applications of the integrated approaches to industrial polymerization processes. The integrated decision making approaches seek to explore the synergy between production schedule design and process unit control to improve process performance. The integration problem has received much attention from both the academia and industry since the past decade. For scheduling, we adopt two formulation approaches based on the state equipment network and resource task network, respectively. For dynamic optimization, we rely on the simultaneous collocation strategy to discretize the differential-algebraic equations. Two integrated formulations are proposed that result in mixed discrete/dynamic models, and solution methods based on decomposition approaches are addressed. A class of ring-opening polymerization processes are used for our industrial case studies. We develop rigorous dynamic reactor models for both semi-batch homopolymerization and copolymerization operations. The reactor models are based on first-principles such as mass and heat balances, reaction kinetics and vapor-liquid equilibria. We derive reactor models with both the population balance method and method of moments. The obtained reactor models are validated using historical plant data. Polymerization recipes are optimized with dynamic optimization algorithms to reduce polymerization times by modifying operating conditions such as the reactor temperature and monomer feed rates over time. Next, we study scheduling methods that involve multiple process units and products. The resource task network scheduling model is reformulated to the state space form that offers a good platform for incorporating dynamic models. Lastly for the integration study, we investigate a process with two parallel polymerization reactors and downstream storage and purification units. The dynamic behaviors of the two reactors are coupled through shared cooling resources. We formulate the integration problem by combining the state space resource task network model with the moment reactor model. The case study results indicate promising improvements of process performances by applying dynamic optimization and scheduling optimization separately, and more importantly, the integration of the two.

Subjects/Keywords: process scheduling; dynamic optimization; process integration; polymerization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Nie, Y. (2014). Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/380

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nie, Yisu. “Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications.” 2014. Thesis, Carnegie Mellon University. Accessed December 07, 2019. http://repository.cmu.edu/dissertations/380.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nie, Yisu. “Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications.” 2014. Web. 07 Dec 2019.

Vancouver:

Nie Y. Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications. [Internet] [Thesis]. Carnegie Mellon University; 2014. [cited 2019 Dec 07]. Available from: http://repository.cmu.edu/dissertations/380.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nie Y. Integration of Scheduling and Dynamic Optimization: Computational Strategies and Industrial Applications. [Thesis]. Carnegie Mellon University; 2014. Available from: http://repository.cmu.edu/dissertations/380

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

29. Vijayachitra S. Studies on process modeling and optimization;.

Degree: Studies on process modeling and optimization, 2015, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/32160

►

Many of the industrial processes are difficult to model because of newlinetheir complex behavior influential characteristics and operational conditions newlineMathematical models of industrial processes provide… (more)

Subjects/Keywords: electrical engineering; optimization; process modeling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

S, V. (2015). Studies on process modeling and optimization;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/32160

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

S, Vijayachitra. “Studies on process modeling and optimization;.” 2015. Thesis, Anna University. Accessed December 07, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/32160.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

S, Vijayachitra. “Studies on process modeling and optimization;.” 2015. Web. 07 Dec 2019.

Vancouver:

S V. Studies on process modeling and optimization;. [Internet] [Thesis]. Anna University; 2015. [cited 2019 Dec 07]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/32160.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

S V. Studies on process modeling and optimization;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/32160

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

30. Briol, Arnaud. Mémoire projet visant à résoudre la surcharge de l’hôpital de jour du CHU Tivoli grâce à la simulation.

Degree: 2016, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/thesis:7123

►

Le but du projet est de fournir au CHU Tivoli une solution à la surcharge de l’hôpital de jour. L’objectif secondaire est de réduire le… (more)

▼

Le but du projet est de fournir au CHU Tivoli une solution à la surcharge de l’hôpital de jour. L’objectif secondaire est de réduire le temps d’attentes des patients avant leur intervention. Dans cette optique, nous démarrons le projet en observant le fonctionnement du service. Ensuite, nous analysons les données de l’année 2015 sur la patientèle du service. Cette analyse nous permet de mettre en lumière qu’il est, actuellement, impossible pour le service d’absorber la demande de patients. En effet, l’hôpital dispose de 24 lits. Or, il y a en moyenne plus de 25 patients par jour. L’organisation actuelle permet au service d’accueillir qu’un patient par lit par jour. Il en résulte que le service est surchargé dans 50,85% des jours. Grâce aux informations collectées au cours de la période préliminaire, nous créons une simulation du trajet du patient dans l’hôpital. Celle-ci permet de tester une organisation alternative du service. Le principe de cette nouvelle organisation est d’hospitaliser les patients en deux vagues distinctes. La première vague arriverait à 7h00 et la seconde à 13h30. L’objectif est de permettre à des patients opérés au matin de sortir dès 13h. Certains lits pourraient alors être utilisés plusieurs fois sur une même journée. La seule contrainte est qu’il faut établir une priorité aux interventions ambulatoires au matin. La simulation a permis de mettre en évidence quelle proportion de patients faire admettre au matin. Bien que 50% d’admission au matin obtienne le taux de surcharge le plus faible, le nombre d’opérations devant être effectué en dehors des heures d’ouverture du bloc à exploser. En effet, nous ferions venir autant de patients au matin que l’après-midi malgré une période d’opération plus courte l’après-midi. La proportion de 60% de patients au matin a donc été conservée. Nous évitons alors de reporter ou retarder plus d’opérations que la situation actuelle. En se faisant, le pourcentage de jours en surcharge est passé à 5,5%-18,5%. Nous ne pouvons actuellement pas déterminer la valeur exacte. Il est seulement possible de déterminer un intervalle. En effet, la valeur exacte dépend du pourcentage de patients sortant à 13h00. En nous référant aux données de l’année 2015, nous avons estimé que ce pourcentage se trouvait entre 20% et 50%. En ce qui concerne le temps d’hospitalisation, il a été réduit de 82 min à 145 min en moyenne. Encore une fois, il dépend du nombre de patients aptes sortir à 13h.

Master [120] en Ingénieur de gestion, Université catholique de Louvain, 2016

Advisors/Committee Members: UCL - Louvain School of Management, Chevalier, Philippe.

Subjects/Keywords: Simulation; hospital; process optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Briol, A. (2016). Mémoire projet visant à résoudre la surcharge de l’hôpital de jour du CHU Tivoli grâce à la simulation. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:7123

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Briol, Arnaud. “Mémoire projet visant à résoudre la surcharge de l’hôpital de jour du CHU Tivoli grâce à la simulation.” 2016. Thesis, Université Catholique de Louvain. Accessed December 07, 2019. http://hdl.handle.net/2078.1/thesis:7123.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Briol, Arnaud. “Mémoire projet visant à résoudre la surcharge de l’hôpital de jour du CHU Tivoli grâce à la simulation.” 2016. Web. 07 Dec 2019.

Vancouver:

Briol A. Mémoire projet visant à résoudre la surcharge de l’hôpital de jour du CHU Tivoli grâce à la simulation. [Internet] [Thesis]. Université Catholique de Louvain; 2016. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/2078.1/thesis:7123.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Briol A. Mémoire projet visant à résoudre la surcharge de l’hôpital de jour du CHU Tivoli grâce à la simulation. [Thesis]. Université Catholique de Louvain; 2016. Available from: http://hdl.handle.net/2078.1/thesis:7123

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations