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You searched for subject:( Gaussian Process Optimization). Showing records 1 – 30 of 37184 total matches.

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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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

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)

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 (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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)

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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)

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 (6th 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 (16th 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 (7th 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

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)

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

Page 1

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APA (6th 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 (16th 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 (7th 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

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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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)

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… 

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Sample image

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APA (6th 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 (16th 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 (7th 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

  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)

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

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APA (6th 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 (16th 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 (7th 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

 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 )… 

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

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

Page 1

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APA (6th 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 (16th 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 (7th 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

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)

Subjects/Keywords: Simulation; hospital; process optimization

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

APA (6th 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 (16th 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 (7th 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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