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You searched for subject:(Polynomial chaos expansion). Showing records 1 – 30 of 37 total matches.

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University of New South Wales

1. Yang, Ji. Stochastic analyses of mechanical and biomedical structures with uncertainties.

Degree: Mechanical & Manufacturing Engineering, 2016, University of New South Wales

 Stochastic models are developed to investigate mechanical and biomedical structures with uncertainties. The natural frequencies and frequency statistics of a plate randomised by point masses… (more)

Subjects/Keywords: Polynomial chaos expansion; Stochastic analyses; Uncertainties

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APA (6th Edition):

Yang, J. (2016). Stochastic analyses of mechanical and biomedical structures with uncertainties. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/55425 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37525/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Yang, Ji. “Stochastic analyses of mechanical and biomedical structures with uncertainties.” 2016. Doctoral Dissertation, University of New South Wales. Accessed March 09, 2021. http://handle.unsw.edu.au/1959.4/55425 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37525/SOURCE02?view=true.

MLA Handbook (7th Edition):

Yang, Ji. “Stochastic analyses of mechanical and biomedical structures with uncertainties.” 2016. Web. 09 Mar 2021.

Vancouver:

Yang J. Stochastic analyses of mechanical and biomedical structures with uncertainties. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2021 Mar 09]. Available from: http://handle.unsw.edu.au/1959.4/55425 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37525/SOURCE02?view=true.

Council of Science Editors:

Yang J. Stochastic analyses of mechanical and biomedical structures with uncertainties. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/55425 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:37525/SOURCE02?view=true


University of Iowa

2. Yadav, Vaibhav. Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems.

Degree: PhD, Mechanical Engineering, 2013, University of Iowa

  The primary objective of this study is to develop new computational methods for solving a general random eigenvalue problem (REP) commonly encountered in modeling… (more)

Subjects/Keywords: ANOVA; Polynomial chaos expansion; Polynomial dimensional decomposition; Random eigenvalues; Stochastic mechanics; Mechanical Engineering

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APA (6th Edition):

Yadav, V. (2013). Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems. (Doctoral Dissertation). University of Iowa. Retrieved from https://ir.uiowa.edu/etd/4927

Chicago Manual of Style (16th Edition):

Yadav, Vaibhav. “Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems.” 2013. Doctoral Dissertation, University of Iowa. Accessed March 09, 2021. https://ir.uiowa.edu/etd/4927.

MLA Handbook (7th Edition):

Yadav, Vaibhav. “Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems.” 2013. Web. 09 Mar 2021.

Vancouver:

Yadav V. Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems. [Internet] [Doctoral dissertation]. University of Iowa; 2013. [cited 2021 Mar 09]. Available from: https://ir.uiowa.edu/etd/4927.

Council of Science Editors:

Yadav V. Novel Computational Methods for Solving High-Dimensional Random Eigenvalue Problems. [Doctoral Dissertation]. University of Iowa; 2013. Available from: https://ir.uiowa.edu/etd/4927


McMaster University

3. Ghaith, Maysara. DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING.

Degree: DEng, 2020, McMaster University

Water is a scarce resource especially as the water demand is significantly increasing due to the rapid growth of population. Hydrological modelling has gained a… (more)

Subjects/Keywords: Hydrology; Forecasting; Uncertainty analysis; Polynomial Chaos expansion; Hybrid modeing

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APA (6th Edition):

Ghaith, M. (2020). DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/25523

Chicago Manual of Style (16th Edition):

Ghaith, Maysara. “DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING.” 2020. Doctoral Dissertation, McMaster University. Accessed March 09, 2021. http://hdl.handle.net/11375/25523.

MLA Handbook (7th Edition):

Ghaith, Maysara. “DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING.” 2020. Web. 09 Mar 2021.

Vancouver:

Ghaith M. DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING. [Internet] [Doctoral dissertation]. McMaster University; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/11375/25523.

Council of Science Editors:

Ghaith M. DEVELOPMENT OF HYBRID APPROACHES FOR UNCERTAINTY QUANTIFICATION IN HYDROLOGICAL MODELING. [Doctoral Dissertation]. McMaster University; 2020. Available from: http://hdl.handle.net/11375/25523


University of Illinois – Urbana-Champaign

4. Anderson, Jesse Cole. Robust design optimization with dynamic constraints using numerical continuation.

Degree: MS, Mechanical Engineering, 2019, University of Illinois – Urbana-Champaign

 This thesis develops a framework for performing robust design optimization of objective functions constrained by differential, algebraic, and integral constraints. A successive parameter continuation method… (more)

Subjects/Keywords: continuation; optimization; robust optimization; polynomial chaos expansion; Duffing oscillator

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APA (6th Edition):

Anderson, J. C. (2019). Robust design optimization with dynamic constraints using numerical continuation. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104728

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):

Anderson, Jesse Cole. “Robust design optimization with dynamic constraints using numerical continuation.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed March 09, 2021. http://hdl.handle.net/2142/104728.

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

MLA Handbook (7th Edition):

Anderson, Jesse Cole. “Robust design optimization with dynamic constraints using numerical continuation.” 2019. Web. 09 Mar 2021.

Vancouver:

Anderson JC. Robust design optimization with dynamic constraints using numerical continuation. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/2142/104728.

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

Council of Science Editors:

Anderson JC. Robust design optimization with dynamic constraints using numerical continuation. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104728

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


Georgia Tech

5. Sinha, Anshuman. Systems design and uncertainty quantification of co2 capture from air.

Degree: PhD, Chemical and Biomolecular Engineering, 2018, Georgia Tech

 The rapid increase in concentration of atmospheric CO2 has stimulated the recent development of CO2 capture technologies. One of the strategy is to capture CO2… (more)

Subjects/Keywords: Direct air capture; Uncertainty quantification; Metal organic frameworks; Polynomial chaos expansion

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APA (6th Edition):

Sinha, A. (2018). Systems design and uncertainty quantification of co2 capture from air. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60214

Chicago Manual of Style (16th Edition):

Sinha, Anshuman. “Systems design and uncertainty quantification of co2 capture from air.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 09, 2021. http://hdl.handle.net/1853/60214.

MLA Handbook (7th Edition):

Sinha, Anshuman. “Systems design and uncertainty quantification of co2 capture from air.” 2018. Web. 09 Mar 2021.

Vancouver:

Sinha A. Systems design and uncertainty quantification of co2 capture from air. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1853/60214.

Council of Science Editors:

Sinha A. Systems design and uncertainty quantification of co2 capture from air. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60214


Delft University of Technology

6. Bailleul, Wouter (author). Using polynomial chaos expansion for wind energy.

Degree: 2018, Delft University of Technology

Surrogate models are used to approximate the expensive ‘true’ simulation codes and thus have the potential to speed up the wind farm layout optimisation problem… (more)

Subjects/Keywords: Polynomial chaos expansion; Wind farm layout optimisation; Genetic algorithm; offshore wind energy; Surrogate modelling

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APA (6th Edition):

Bailleul, W. (. (2018). Using polynomial chaos expansion for wind energy. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:4bd08f5c-44a2-40c6-a163-8914ea556d0e

Chicago Manual of Style (16th Edition):

Bailleul, Wouter (author). “Using polynomial chaos expansion for wind energy.” 2018. Masters Thesis, Delft University of Technology. Accessed March 09, 2021. http://resolver.tudelft.nl/uuid:4bd08f5c-44a2-40c6-a163-8914ea556d0e.

MLA Handbook (7th Edition):

Bailleul, Wouter (author). “Using polynomial chaos expansion for wind energy.” 2018. Web. 09 Mar 2021.

Vancouver:

Bailleul W(. Using polynomial chaos expansion for wind energy. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Mar 09]. Available from: http://resolver.tudelft.nl/uuid:4bd08f5c-44a2-40c6-a163-8914ea556d0e.

Council of Science Editors:

Bailleul W(. Using polynomial chaos expansion for wind energy. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:4bd08f5c-44a2-40c6-a163-8914ea556d0e


University of Washington

7. Liu, Zhanlin. Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification.

Degree: PhD, 2020, University of Washington

 Uncertainties exist in both physics-based and data-driven models of systems. Understanding how system inputs affect a system output's uncertainty is essential to improve system outputs… (more)

Subjects/Keywords: data-driven; polynomial chaos expansion; sensitivity analysis; uncertainty quantification; Industrial engineering; Statistics; Engineering; Industrial engineering

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APA (6th Edition):

Liu, Z. (2020). Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/46491

Chicago Manual of Style (16th Edition):

Liu, Zhanlin. “Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification.” 2020. Doctoral Dissertation, University of Washington. Accessed March 09, 2021. http://hdl.handle.net/1773/46491.

MLA Handbook (7th Edition):

Liu, Zhanlin. “Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification.” 2020. Web. 09 Mar 2021.

Vancouver:

Liu Z. Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification. [Internet] [Doctoral dissertation]. University of Washington; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/1773/46491.

Council of Science Editors:

Liu Z. Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification. [Doctoral Dissertation]. University of Washington; 2020. Available from: http://hdl.handle.net/1773/46491


University of New Mexico

8. Olson, Aaron J. Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media.

Degree: Nuclear Engineering, 2016, University of New Mexico

  The ability to perform radiation transport computations in stochastic media is essential for predictive capabilities in applications such as weather modeling, radiation shielding involving… (more)

Subjects/Keywords: Karhunen-Loève; Monte Carlo; Polynomial Chaos Expansion; Nataf Transformation; Stochastic Collocation; Nyström Method; Nuclear Engineering

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APA (6th Edition):

Olson, A. J. (2016). Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media. (Doctoral Dissertation). University of New Mexico. Retrieved from https://digitalrepository.unm.edu/ne_etds/55

Chicago Manual of Style (16th Edition):

Olson, Aaron J. “Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media.” 2016. Doctoral Dissertation, University of New Mexico. Accessed March 09, 2021. https://digitalrepository.unm.edu/ne_etds/55.

MLA Handbook (7th Edition):

Olson, Aaron J. “Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media.” 2016. Web. 09 Mar 2021.

Vancouver:

Olson AJ. Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media. [Internet] [Doctoral dissertation]. University of New Mexico; 2016. [cited 2021 Mar 09]. Available from: https://digitalrepository.unm.edu/ne_etds/55.

Council of Science Editors:

Olson AJ. Reduced-Order Monte Carlo Modeling of Radiation Transport in Random Media. [Doctoral Dissertation]. University of New Mexico; 2016. Available from: https://digitalrepository.unm.edu/ne_etds/55


Duke University

9. Winokur, Justin Gregory. Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification .

Degree: 2015, Duke University

Polynomial chaos expansions provide an efficient and robust framework to analyze and quantify uncertainty in computational models. This dissertation explores the use of adaptive… (more)

Subjects/Keywords: Mechanical engineering; Applied mathematics; adaptive methods; polynomial chaos expansion; sparse grids; uncertainty quantification

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APA (6th Edition):

Winokur, J. G. (2015). Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/9845

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):

Winokur, Justin Gregory. “Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification .” 2015. Thesis, Duke University. Accessed March 09, 2021. http://hdl.handle.net/10161/9845.

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

MLA Handbook (7th Edition):

Winokur, Justin Gregory. “Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification .” 2015. Web. 09 Mar 2021.

Vancouver:

Winokur JG. Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification . [Internet] [Thesis]. Duke University; 2015. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10161/9845.

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

Council of Science Editors:

Winokur JG. Adaptive Sparse Grid Approaches to Polynomial Chaos Expansions for Uncertainty Quantification . [Thesis]. Duke University; 2015. Available from: http://hdl.handle.net/10161/9845

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


Virginia Tech

10. Scott, Karen Mary Louise. Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads.

Degree: PhD, Aerospace and Ocean Engineering, 2011, Virginia Tech

 There is a need to investigate and improve upon existing methods to predict response of sensors due to flow-induced vibrations in a pipe flow. The… (more)

Subjects/Keywords: Flow-induced vibration; random vibration; non-stationary forcing; Karhunen-Loeve expansion; polynomial chaos

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APA (6th Edition):

Scott, K. M. L. (2011). Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/38686

Chicago Manual of Style (16th Edition):

Scott, Karen Mary Louise. “Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads.” 2011. Doctoral Dissertation, Virginia Tech. Accessed March 09, 2021. http://hdl.handle.net/10919/38686.

MLA Handbook (7th Edition):

Scott, Karen Mary Louise. “Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads.” 2011. Web. 09 Mar 2021.

Vancouver:

Scott KML. Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10919/38686.

Council of Science Editors:

Scott KML. Practical Analysis Tools for Structures Subjected to Flow-Induced and Non-Stationary Random Loads. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/38686


INP Toulouse

11. El Moçayd, Nabil. La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale : Polynomial chaos expansion in fluvial hydraulics in Ensemble data assimilation framework.

Degree: Docteur es, Surfaces Interfaces Continentales Hydrologie, 2017, INP Toulouse

 Ce travail porte sur la construction d'un modèle réduit en hydraulique fluviale avec une méthode de décomposition en polynôme du chaos. Ce modèle réduit remplace… (more)

Subjects/Keywords: Polynômes du chaos; Quantification des incertitudes; Assimilation de données; Hydraulique fluviale; Chaos polynomial expansion; Hydraulics; Data assimilation; Uncertainty quantification; 551

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APA (6th Edition):

El Moçayd, N. (2017). La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale : Polynomial chaos expansion in fluvial hydraulics in Ensemble data assimilation framework. (Doctoral Dissertation). INP Toulouse. Retrieved from http://www.theses.fr/2017INPT0020

Chicago Manual of Style (16th Edition):

El Moçayd, Nabil. “La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale : Polynomial chaos expansion in fluvial hydraulics in Ensemble data assimilation framework.” 2017. Doctoral Dissertation, INP Toulouse. Accessed March 09, 2021. http://www.theses.fr/2017INPT0020.

MLA Handbook (7th Edition):

El Moçayd, Nabil. “La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale : Polynomial chaos expansion in fluvial hydraulics in Ensemble data assimilation framework.” 2017. Web. 09 Mar 2021.

Vancouver:

El Moçayd N. La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale : Polynomial chaos expansion in fluvial hydraulics in Ensemble data assimilation framework. [Internet] [Doctoral dissertation]. INP Toulouse; 2017. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2017INPT0020.

Council of Science Editors:

El Moçayd N. La décomposition en polynôme du chaos pour l'amélioration de l'assimilation de données ensembliste en hydraulique fluviale : Polynomial chaos expansion in fluvial hydraulics in Ensemble data assimilation framework. [Doctoral Dissertation]. INP Toulouse; 2017. Available from: http://www.theses.fr/2017INPT0020

12. Fajraoui, Noura. Analyse de sensibilité globale et polynômes de chaos pour l'estimation des paramètres : application aux transferts en milieu poreux : Sensitivity analysis and polynomial chaos expansion for parameter estimation : application to transfer in porous media.

Degree: Docteur es, Mécanique des fluides, 2014, Université de Strasbourg

La gestion des transferts des contaminants en milieu poreux représentent une préoccupation croissante et revêtent un intérêt particulier pour le contrôle de la pollution dans… (more)

Subjects/Keywords: Polynômes de chaos; Analyse de sensibilité; Estimation des paramètres; Polynomial chaos expansion; Sensitivity analysis; Parameter estimation; 532

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APA (6th Edition):

Fajraoui, N. (2014). Analyse de sensibilité globale et polynômes de chaos pour l'estimation des paramètres : application aux transferts en milieu poreux : Sensitivity analysis and polynomial chaos expansion for parameter estimation : application to transfer in porous media. (Doctoral Dissertation). Université de Strasbourg. Retrieved from http://www.theses.fr/2014STRAH001

Chicago Manual of Style (16th Edition):

Fajraoui, Noura. “Analyse de sensibilité globale et polynômes de chaos pour l'estimation des paramètres : application aux transferts en milieu poreux : Sensitivity analysis and polynomial chaos expansion for parameter estimation : application to transfer in porous media.” 2014. Doctoral Dissertation, Université de Strasbourg. Accessed March 09, 2021. http://www.theses.fr/2014STRAH001.

MLA Handbook (7th Edition):

Fajraoui, Noura. “Analyse de sensibilité globale et polynômes de chaos pour l'estimation des paramètres : application aux transferts en milieu poreux : Sensitivity analysis and polynomial chaos expansion for parameter estimation : application to transfer in porous media.” 2014. Web. 09 Mar 2021.

Vancouver:

Fajraoui N. Analyse de sensibilité globale et polynômes de chaos pour l'estimation des paramètres : application aux transferts en milieu poreux : Sensitivity analysis and polynomial chaos expansion for parameter estimation : application to transfer in porous media. [Internet] [Doctoral dissertation]. Université de Strasbourg; 2014. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2014STRAH001.

Council of Science Editors:

Fajraoui N. Analyse de sensibilité globale et polynômes de chaos pour l'estimation des paramètres : application aux transferts en milieu poreux : Sensitivity analysis and polynomial chaos expansion for parameter estimation : application to transfer in porous media. [Doctoral Dissertation]. Université de Strasbourg; 2014. Available from: http://www.theses.fr/2014STRAH001


University of Technology, Sydney

13. Wu, Jinglai. Uncertainty analysis and optimization by using the orthogonal polynomials.

Degree: 2015, University of Technology, Sydney

 Engineering problems are generally described by mathematic models, and the parameters in mathematic models are usually assumed to be deterministic when solving these models. However,… (more)

Subjects/Keywords: Uncertain parameters.; Aleatory (random) uncertainty.; Epistemic uncertainty.; Polynomial Chaos (PC) expansion theory.; Chebyshev polynomials approximation theory.; Hybrid uncertainty analysis.; Polynomial-Chaos-Chebyshev-Interval (PCCI) method.

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APA (6th Edition):

Wu, J. (2015). Uncertainty analysis and optimization by using the orthogonal polynomials. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/43498

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):

Wu, Jinglai. “Uncertainty analysis and optimization by using the orthogonal polynomials.” 2015. Thesis, University of Technology, Sydney. Accessed March 09, 2021. http://hdl.handle.net/10453/43498.

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

MLA Handbook (7th Edition):

Wu, Jinglai. “Uncertainty analysis and optimization by using the orthogonal polynomials.” 2015. Web. 09 Mar 2021.

Vancouver:

Wu J. Uncertainty analysis and optimization by using the orthogonal polynomials. [Internet] [Thesis]. University of Technology, Sydney; 2015. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10453/43498.

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

Council of Science Editors:

Wu J. Uncertainty analysis and optimization by using the orthogonal polynomials. [Thesis]. University of Technology, Sydney; 2015. Available from: http://hdl.handle.net/10453/43498

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


University of Colorado

14. Hadigol, Mohammad. Uncertainty Quantification of Coupled Problems with Applications to Lithium-ion Batteries.

Degree: PhD, Aerospace Engineering Sciences, 2016, University of Colorado

  This thesis includes three main parts that are concerned with the propagation of uncertainty across high-dimensional coupled problems with applications to Lithium-ion batteries (LIBs).… (more)

Subjects/Keywords: Coupled Domain; High-dimensional; Lithium-ion Battery; Optimal Sampling; Polynomial Chaos Expansion; Uncertainty Quantification; Applied Mathematics; Engineering; Power and Energy

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APA (6th Edition):

Hadigol, M. (2016). Uncertainty Quantification of Coupled Problems with Applications to Lithium-ion Batteries. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/asen_gradetds/137

Chicago Manual of Style (16th Edition):

Hadigol, Mohammad. “Uncertainty Quantification of Coupled Problems with Applications to Lithium-ion Batteries.” 2016. Doctoral Dissertation, University of Colorado. Accessed March 09, 2021. https://scholar.colorado.edu/asen_gradetds/137.

MLA Handbook (7th Edition):

Hadigol, Mohammad. “Uncertainty Quantification of Coupled Problems with Applications to Lithium-ion Batteries.” 2016. Web. 09 Mar 2021.

Vancouver:

Hadigol M. Uncertainty Quantification of Coupled Problems with Applications to Lithium-ion Batteries. [Internet] [Doctoral dissertation]. University of Colorado; 2016. [cited 2021 Mar 09]. Available from: https://scholar.colorado.edu/asen_gradetds/137.

Council of Science Editors:

Hadigol M. Uncertainty Quantification of Coupled Problems with Applications to Lithium-ion Batteries. [Doctoral Dissertation]. University of Colorado; 2016. Available from: https://scholar.colorado.edu/asen_gradetds/137


University of Colorado

15. Peng, Ji. Uncertainty Quantification via Sparse Polynomial Chaos Expansion.

Degree: PhD, Mechanical Engineering, 2015, University of Colorado

  Uncertainty quantification (UQ) is an emerging research area that aims to develop methods for accurate predictions of quantities of interest (QoI's) from complex engineering… (more)

Subjects/Keywords: Basis design; Compressive sampling; Polynomial chaos expansion; Sparse approximation; Uncertainty quantification; Applied Mathematics; Mechanical Engineering; Statistics and Probability

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APA (6th Edition):

Peng, J. (2015). Uncertainty Quantification via Sparse Polynomial Chaos Expansion. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/mcen_gradetds/112

Chicago Manual of Style (16th Edition):

Peng, Ji. “Uncertainty Quantification via Sparse Polynomial Chaos Expansion.” 2015. Doctoral Dissertation, University of Colorado. Accessed March 09, 2021. https://scholar.colorado.edu/mcen_gradetds/112.

MLA Handbook (7th Edition):

Peng, Ji. “Uncertainty Quantification via Sparse Polynomial Chaos Expansion.” 2015. Web. 09 Mar 2021.

Vancouver:

Peng J. Uncertainty Quantification via Sparse Polynomial Chaos Expansion. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2021 Mar 09]. Available from: https://scholar.colorado.edu/mcen_gradetds/112.

Council of Science Editors:

Peng J. Uncertainty Quantification via Sparse Polynomial Chaos Expansion. [Doctoral Dissertation]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/mcen_gradetds/112


University of Southern California

16. Keshavarzzadeh, Vahid. Design optimization under uncertainty for rotor blades of horizontal axis wind turbines.

Degree: PhD, Civil Engineering (Structural Engineering), 2016, University of Southern California

 This dissertation is motivated by challenges involved in the design optimization under uncertainty for the structures undergoing fluid structure interaction phenomena. In particular, this study… (more)

Subjects/Keywords: aerodynamic optimization; blade element method; reliability based design optimization; polynomial chaos expansion; convergence acceleration; stochastic differential equation; nonlinear sequence transformation

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APA (6th Edition):

Keshavarzzadeh, V. (2016). Design optimization under uncertainty for rotor blades of horizontal axis wind turbines. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422612/rec/1872

Chicago Manual of Style (16th Edition):

Keshavarzzadeh, Vahid. “Design optimization under uncertainty for rotor blades of horizontal axis wind turbines.” 2016. Doctoral Dissertation, University of Southern California. Accessed March 09, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422612/rec/1872.

MLA Handbook (7th Edition):

Keshavarzzadeh, Vahid. “Design optimization under uncertainty for rotor blades of horizontal axis wind turbines.” 2016. Web. 09 Mar 2021.

Vancouver:

Keshavarzzadeh V. Design optimization under uncertainty for rotor blades of horizontal axis wind turbines. [Internet] [Doctoral dissertation]. University of Southern California; 2016. [cited 2021 Mar 09]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422612/rec/1872.

Council of Science Editors:

Keshavarzzadeh V. Design optimization under uncertainty for rotor blades of horizontal axis wind turbines. [Doctoral Dissertation]. University of Southern California; 2016. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/422612/rec/1872


University of Texas – Austin

17. -3842-5090. Uncertainty propagation and conjunction assessment for resident space objects.

Degree: PhD, Aerospace engineering, 2015, University of Texas – Austin

 Presently, the catalog of Resident Space Objects (RSOs) in Earth orbit tracked by the U.S. Space Surveillance Network (SSN) is greater than 21,000 objects. The… (more)

Subjects/Keywords: Uncertainty quantification; Polynomial chaos expansion; Gaussian mixture model; Uncertainty propagation; Monte Carlo simulation; Graphics processing unit; Parallel computing

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APA (6th Edition):

-3842-5090. (2015). Uncertainty propagation and conjunction assessment for resident space objects. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32906

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-3842-5090. “Uncertainty propagation and conjunction assessment for resident space objects.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed March 09, 2021. http://hdl.handle.net/2152/32906.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-3842-5090. “Uncertainty propagation and conjunction assessment for resident space objects.” 2015. Web. 09 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3842-5090. Uncertainty propagation and conjunction assessment for resident space objects. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/2152/32906.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-3842-5090. Uncertainty propagation and conjunction assessment for resident space objects. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32906

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

18. Hu, Junjun. Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods.

Degree: PhD, 2015, University of Oklahoma

 In the context of prediction science, the sources of uncertainty can be from the uncertainties of the experiments, modeling, model inputs, numerical analysis, etc. This… (more)

Subjects/Keywords: Uncertainty Quantification; Polynomial Chaos Expansion; Unscented Transformation

…namely classical Monte Carlo (MC), polynomial chaos (PC) expansion and… …polynomial chaos (PC) expansion. It is known that the solution of a model with random… …methods and some conclusions are provided. 7 Chapter 2 Polynomial Chaos Expansion As one of… …the most widely used non-sampling techniques, the polynomial chaos (PC) expansion… …the concept and proposed the generalized polynomial chaos (gPC) expansion or… 

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APA (6th Edition):

Hu, J. (2015). Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/21722

Chicago Manual of Style (16th Edition):

Hu, Junjun. “Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods.” 2015. Doctoral Dissertation, University of Oklahoma. Accessed March 09, 2021. http://hdl.handle.net/11244/21722.

MLA Handbook (7th Edition):

Hu, Junjun. “Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods.” 2015. Web. 09 Mar 2021.

Vancouver:

Hu J. Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods. [Internet] [Doctoral dissertation]. University of Oklahoma; 2015. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/11244/21722.

Council of Science Editors:

Hu J. Forecast Uncertainty Quantification using Monte Carlo, Polynomial Chaos Expansion and Unscented Transformation Methods. [Doctoral Dissertation]. University of Oklahoma; 2015. Available from: http://hdl.handle.net/11244/21722


Delft University of Technology

19. Chuang, Jia Shian (author). Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models.

Degree: 2020, Delft University of Technology

This research aims to develop a method to create a wind farm layout that is robust against the uncertainty source, the inter-year variation of Weibull… (more)

Subjects/Keywords: Wind farm layout optimisation; Uncertainty Quantification; Surrogate modelling; Polynomial Chaos Expansion; Kriging; Genetic algorithm; Offshore wind

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APA (6th Edition):

Chuang, J. S. (. (2020). Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6a87eddd-7c1b-44b6-88c1-5988d40237a2

Chicago Manual of Style (16th Edition):

Chuang, Jia Shian (author). “Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models.” 2020. Masters Thesis, Delft University of Technology. Accessed March 09, 2021. http://resolver.tudelft.nl/uuid:6a87eddd-7c1b-44b6-88c1-5988d40237a2.

MLA Handbook (7th Edition):

Chuang, Jia Shian (author). “Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models.” 2020. Web. 09 Mar 2021.

Vancouver:

Chuang JS(. Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Mar 09]. Available from: http://resolver.tudelft.nl/uuid:6a87eddd-7c1b-44b6-88c1-5988d40237a2.

Council of Science Editors:

Chuang JS(. Wind Farm Layout Optimisation Under Uncertainty Using Surrogate Models. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:6a87eddd-7c1b-44b6-88c1-5988d40237a2

20. Segui Vasquez, Bartolomé. Modélisation dynamique des systèmes disque aubes multi-étages : Effets des incertitudes : Dynamic modeling of multistage blade disk systems : Effects of uncertainties.

Degree: Docteur es, Génie mécanique, 2013, INSA Lyon

Les conceptions récentes de turbomachines ont tendance à évoluer vers des liaisons entre étages de plus en plus souples et des niveaux d'amortissement faibles, donnant… (more)

Subjects/Keywords: Mécanique appliquée; Liaisons mécaniques; Turbomachines; Dynamique des disques aubés; Symétrie cyclique multi-étage; Analys modale aléatoire; Expansion en chaos polynomial; Bladed disc dynamics; Multi-stage cyclic symetry; Stochastic modal analysis; Polynomial chaos expansion; 621.850 72

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APA (6th Edition):

Segui Vasquez, B. (2013). Modélisation dynamique des systèmes disque aubes multi-étages : Effets des incertitudes : Dynamic modeling of multistage blade disk systems : Effects of uncertainties. (Doctoral Dissertation). INSA Lyon. Retrieved from http://www.theses.fr/2013ISAL0057

Chicago Manual of Style (16th Edition):

Segui Vasquez, Bartolomé. “Modélisation dynamique des systèmes disque aubes multi-étages : Effets des incertitudes : Dynamic modeling of multistage blade disk systems : Effects of uncertainties.” 2013. Doctoral Dissertation, INSA Lyon. Accessed March 09, 2021. http://www.theses.fr/2013ISAL0057.

MLA Handbook (7th Edition):

Segui Vasquez, Bartolomé. “Modélisation dynamique des systèmes disque aubes multi-étages : Effets des incertitudes : Dynamic modeling of multistage blade disk systems : Effects of uncertainties.” 2013. Web. 09 Mar 2021.

Vancouver:

Segui Vasquez B. Modélisation dynamique des systèmes disque aubes multi-étages : Effets des incertitudes : Dynamic modeling of multistage blade disk systems : Effects of uncertainties. [Internet] [Doctoral dissertation]. INSA Lyon; 2013. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2013ISAL0057.

Council of Science Editors:

Segui Vasquez B. Modélisation dynamique des systèmes disque aubes multi-étages : Effets des incertitudes : Dynamic modeling of multistage blade disk systems : Effects of uncertainties. [Doctoral Dissertation]. INSA Lyon; 2013. Available from: http://www.theses.fr/2013ISAL0057


University of Waterloo

21. Kimaev, Grigoriy. On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems.

Degree: 2020, University of Waterloo

 In order to better understand and leverage natural phenomena to design materials and devices (e.g. biomedical coatings, catalytic reactors, thin conductive films for microprocessors, etc.),… (more)

Subjects/Keywords: uncertainty quantification; stochastic multiscale systems; polynomial chaos expansion; power series expansion; multilevel Monte Carlo sampling; model predictive control; shrinking horizon optimization; Artificial Neural Networks

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APA (6th Edition):

Kimaev, G. (2020). On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16160

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):

Kimaev, Grigoriy. “On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems.” 2020. Thesis, University of Waterloo. Accessed March 09, 2021. http://hdl.handle.net/10012/16160.

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

MLA Handbook (7th Edition):

Kimaev, Grigoriy. “On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems.” 2020. Web. 09 Mar 2021.

Vancouver:

Kimaev G. On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10012/16160.

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

Council of Science Editors:

Kimaev G. On the Techniques for Efficient Sampling, Uncertainty Quantification and Robust Control of Stochastic Multiscale Systems. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16160

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

22. Braun, Mathias. Reduced Order Modelling and Uncertainty Propagation Applied to Water Distribution Networks : Modélisation réduite et propagation d’incertitudes pour les réseaux d’alimentation en eau potable.

Degree: Docteur es, Mathématiques appliquées et calcul scientifique, 2019, Bordeaux

Les réseaux de distribution d’eau consistent en de grandes infrastructures réparties dans l’espace qui assurent la distribution d’eau potable en quantité et en qualité suffisantes.… (more)

Subjects/Keywords: Modèles d’ingénierie; Développement de chaos polynomial; Quantification d’incertitude; Méthodes basées sur la projection; Modèle d’ordre réduit; Réseaux de distribution d’eau; Engineering models; Polynomial chaos expansion; Uncertainty quantification; Projection based methods; Reduced order model; Water distribution networks

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APA (6th Edition):

Braun, M. (2019). Reduced Order Modelling and Uncertainty Propagation Applied to Water Distribution Networks : Modélisation réduite et propagation d’incertitudes pour les réseaux d’alimentation en eau potable. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2019BORD0050

Chicago Manual of Style (16th Edition):

Braun, Mathias. “Reduced Order Modelling and Uncertainty Propagation Applied to Water Distribution Networks : Modélisation réduite et propagation d’incertitudes pour les réseaux d’alimentation en eau potable.” 2019. Doctoral Dissertation, Bordeaux. Accessed March 09, 2021. http://www.theses.fr/2019BORD0050.

MLA Handbook (7th Edition):

Braun, Mathias. “Reduced Order Modelling and Uncertainty Propagation Applied to Water Distribution Networks : Modélisation réduite et propagation d’incertitudes pour les réseaux d’alimentation en eau potable.” 2019. Web. 09 Mar 2021.

Vancouver:

Braun M. Reduced Order Modelling and Uncertainty Propagation Applied to Water Distribution Networks : Modélisation réduite et propagation d’incertitudes pour les réseaux d’alimentation en eau potable. [Internet] [Doctoral dissertation]. Bordeaux; 2019. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2019BORD0050.

Council of Science Editors:

Braun M. Reduced Order Modelling and Uncertainty Propagation Applied to Water Distribution Networks : Modélisation réduite et propagation d’incertitudes pour les réseaux d’alimentation en eau potable. [Doctoral Dissertation]. Bordeaux; 2019. Available from: http://www.theses.fr/2019BORD0050

23. Kouassi, Attibaud. Propagation d'incertitudes en CEM. Application à l'analyse de fiabilité et de sensibilité de lignes de transmission et d'antennes : Uncertainty propagation in EMC. Application to reliability and sensitivity analyzes of transmission lines and antennas.

Degree: Docteur es, Electromagnétisme, 2017, Université Clermont Auvergne‎ (2017-2020)

 De nos jours, la plupart des analyses CEM d’équipements et systèmes électroniques sont basées sur des approches quasi-déterministes dans lesquelles les paramètres internes et externes… (more)

Subjects/Keywords: Compatibilité électromagnétique; Propagation d’incertitudes; Analyse de fiabilité; Analyse de sensibilité; Lignes de transmission; Antennes; Développement en polynômes chaos; Développement de Karhunen-Loève; Équations des télégraphistes stochastiques; Electromagnetic compatibility; Uncertainty propagation; Reliability analysis; Sensitivity analysis; Transmission lines; Antennas; Polynomial chaos expansion; Karhunen-Loève expansion; Stochastic telegrapher equations

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APA (6th Edition):

Kouassi, A. (2017). Propagation d'incertitudes en CEM. Application à l'analyse de fiabilité et de sensibilité de lignes de transmission et d'antennes : Uncertainty propagation in EMC. Application to reliability and sensitivity analyzes of transmission lines and antennas. (Doctoral Dissertation). Université Clermont Auvergne‎ (2017-2020). Retrieved from http://www.theses.fr/2017CLFAC067

Chicago Manual of Style (16th Edition):

Kouassi, Attibaud. “Propagation d'incertitudes en CEM. Application à l'analyse de fiabilité et de sensibilité de lignes de transmission et d'antennes : Uncertainty propagation in EMC. Application to reliability and sensitivity analyzes of transmission lines and antennas.” 2017. Doctoral Dissertation, Université Clermont Auvergne‎ (2017-2020). Accessed March 09, 2021. http://www.theses.fr/2017CLFAC067.

MLA Handbook (7th Edition):

Kouassi, Attibaud. “Propagation d'incertitudes en CEM. Application à l'analyse de fiabilité et de sensibilité de lignes de transmission et d'antennes : Uncertainty propagation in EMC. Application to reliability and sensitivity analyzes of transmission lines and antennas.” 2017. Web. 09 Mar 2021.

Vancouver:

Kouassi A. Propagation d'incertitudes en CEM. Application à l'analyse de fiabilité et de sensibilité de lignes de transmission et d'antennes : Uncertainty propagation in EMC. Application to reliability and sensitivity analyzes of transmission lines and antennas. [Internet] [Doctoral dissertation]. Université Clermont Auvergne‎ (2017-2020); 2017. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2017CLFAC067.

Council of Science Editors:

Kouassi A. Propagation d'incertitudes en CEM. Application à l'analyse de fiabilité et de sensibilité de lignes de transmission et d'antennes : Uncertainty propagation in EMC. Application to reliability and sensitivity analyzes of transmission lines and antennas. [Doctoral Dissertation]. Université Clermont Auvergne‎ (2017-2020); 2017. Available from: http://www.theses.fr/2017CLFAC067


University of Alberta

24. Zhao, Fuqiong. Integrated Prognostics for Component Health Management.

Degree: PhD, Department of Mechanical Engineering, 2015, University of Alberta

 Prognostics in engineering field is dedicated to predicting how long further a component or a system will perform their intended functions before failure. Prognostics is… (more)

Subjects/Keywords: Stochastic Collocation; Uncertainty Quantification; Condition Monitoring; Crack Initiation Time; Bayesian Inference; Gear; Crack Propagation; Polynomial Chaos Expansion; Surface Wear; Shock; Remaining Useful Life; Integrated Prognostics; Time-varying Operating Conditions

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APA (6th Edition):

Zhao, F. (2015). Integrated Prognostics for Component Health Management. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cst74cq66z

Chicago Manual of Style (16th Edition):

Zhao, Fuqiong. “Integrated Prognostics for Component Health Management.” 2015. Doctoral Dissertation, University of Alberta. Accessed March 09, 2021. https://era.library.ualberta.ca/files/cst74cq66z.

MLA Handbook (7th Edition):

Zhao, Fuqiong. “Integrated Prognostics for Component Health Management.” 2015. Web. 09 Mar 2021.

Vancouver:

Zhao F. Integrated Prognostics for Component Health Management. [Internet] [Doctoral dissertation]. University of Alberta; 2015. [cited 2021 Mar 09]. Available from: https://era.library.ualberta.ca/files/cst74cq66z.

Council of Science Editors:

Zhao F. Integrated Prognostics for Component Health Management. [Doctoral Dissertation]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/cst74cq66z


Brno University of Technology

25. Svobodová, Miriam. Dynamika soustav těles s neurčitostním modelem vzájemné vazby.

Degree: 2020, Brno University of Technology

 This diploma thesis deal with evaluation of the impact in the scale of uncertaintly stiffness on the tool deviation during grooving process. By the affect… (more)

Subjects/Keywords: Neurčitost; analýza neurčitosti; citlivostní analýza; Monte Carlo; expanze polynomiálního chaosu; Sobolova metoda; soustružení; Uncertainty; uncertainty analysis; sensitivity analysis; Monte Carlo; polynomial chaos expansion; Sobol's method; turning

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APA (6th Edition):

Svobodová, M. (2020). Dynamika soustav těles s neurčitostním modelem vzájemné vazby. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/193632

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):

Svobodová, Miriam. “Dynamika soustav těles s neurčitostním modelem vzájemné vazby.” 2020. Thesis, Brno University of Technology. Accessed March 09, 2021. http://hdl.handle.net/11012/193632.

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

MLA Handbook (7th Edition):

Svobodová, Miriam. “Dynamika soustav těles s neurčitostním modelem vzájemné vazby.” 2020. Web. 09 Mar 2021.

Vancouver:

Svobodová M. Dynamika soustav těles s neurčitostním modelem vzájemné vazby. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/11012/193632.

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

Council of Science Editors:

Svobodová M. Dynamika soustav těles s neurčitostním modelem vzájemné vazby. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/193632

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


University of Illinois – Urbana-Champaign

26. Wu, Xu. Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework.

Degree: PhD, Nuclear, Plasma, Radiolgc Engr, 2017, University of Illinois – Urbana-Champaign

 Mathematical modeling and computer simulations have long been the central technical topics in practically all branches of science and technology. Tremendous progress has been achieved… (more)

Subjects/Keywords: Inverse Uncertainty Quantification; Bayesian Calibration; Bayesian Analysis; Model Discrepancy; Modular Bayesian Approach; Metamodel; Surrogate Model; Gaussian Process; Polynomial Chaos Expansion; Sparse Grid Stochastic Collocation

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APA (6th Edition):

Wu, X. (2017). Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99335

Chicago Manual of Style (16th Edition):

Wu, Xu. “Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 09, 2021. http://hdl.handle.net/2142/99335.

MLA Handbook (7th Edition):

Wu, Xu. “Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework.” 2017. Web. 09 Mar 2021.

Vancouver:

Wu X. Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/2142/99335.

Council of Science Editors:

Wu X. Metamodel-based inverse uncertainty quantification of nuclear reactor simulators under the Bayesian framework. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99335


Virginia Tech

27. Price, Darryl Brian. Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions.

Degree: MS, Mechanical Engineering, 2008, Virginia Tech

 The main goal of this study is the use of polynomial chaos expansion (PCE) to analyze the uncertainty in calculating the lateral and longitudinal center… (more)

Subjects/Keywords: center of gravity; 8-post test; polynomial chaos expansion; Galerkin method

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APA (6th Edition):

Price, D. B. (2008). Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/33625

Chicago Manual of Style (16th Edition):

Price, Darryl Brian. “Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions.” 2008. Masters Thesis, Virginia Tech. Accessed March 09, 2021. http://hdl.handle.net/10919/33625.

MLA Handbook (7th Edition):

Price, Darryl Brian. “Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions.” 2008. Web. 09 Mar 2021.

Vancouver:

Price DB. Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions. [Internet] [Masters thesis]. Virginia Tech; 2008. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10919/33625.

Council of Science Editors:

Price DB. Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions. [Masters Thesis]. Virginia Tech; 2008. Available from: http://hdl.handle.net/10919/33625

28. Kassir, Wafaa. Approche probabiliste non gaussienne des charges statiques équivalentes des effets du vent en dynamique des structures à partir de mesures en soufflerie : A non-Gaussian probabilistic approach for the equivalent static loads of wind effects in structural dynamics from wind tunnel measurements.

Degree: Docteur es, Génie Civil, 2017, Université Paris-Est

Afin d'estimer les forces statiques équivalentes du vent, qui produisent les réponses quasi-statiques et dynamiques extrêmes dans les structures soumises au champ de pression instationnaire… (more)

Subjects/Keywords: Force statique équivalente du vent; Champ de pression instationnaire non gaussien; Représentation en chaos polynomiaux; Réponses quasi-Statiques; Dynamique stochastique; Statistiques des valeurs extrêmes; Equivalent static wind loads; Non-Gaussian unsteady pressure field; Polynomial chaos expansion; Quasi-Static responses; Stochastic dynamics; Extreme value statistics

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

APA (6th Edition):

Kassir, W. (2017). Approche probabiliste non gaussienne des charges statiques équivalentes des effets du vent en dynamique des structures à partir de mesures en soufflerie : A non-Gaussian probabilistic approach for the equivalent static loads of wind effects in structural dynamics from wind tunnel measurements. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2017PESC1116

Chicago Manual of Style (16th Edition):

Kassir, Wafaa. “Approche probabiliste non gaussienne des charges statiques équivalentes des effets du vent en dynamique des structures à partir de mesures en soufflerie : A non-Gaussian probabilistic approach for the equivalent static loads of wind effects in structural dynamics from wind tunnel measurements.” 2017. Doctoral Dissertation, Université Paris-Est. Accessed March 09, 2021. http://www.theses.fr/2017PESC1116.

MLA Handbook (7th Edition):

Kassir, Wafaa. “Approche probabiliste non gaussienne des charges statiques équivalentes des effets du vent en dynamique des structures à partir de mesures en soufflerie : A non-Gaussian probabilistic approach for the equivalent static loads of wind effects in structural dynamics from wind tunnel measurements.” 2017. Web. 09 Mar 2021.

Vancouver:

Kassir W. Approche probabiliste non gaussienne des charges statiques équivalentes des effets du vent en dynamique des structures à partir de mesures en soufflerie : A non-Gaussian probabilistic approach for the equivalent static loads of wind effects in structural dynamics from wind tunnel measurements. [Internet] [Doctoral dissertation]. Université Paris-Est; 2017. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2017PESC1116.

Council of Science Editors:

Kassir W. Approche probabiliste non gaussienne des charges statiques équivalentes des effets du vent en dynamique des structures à partir de mesures en soufflerie : A non-Gaussian probabilistic approach for the equivalent static loads of wind effects in structural dynamics from wind tunnel measurements. [Doctoral Dissertation]. Université Paris-Est; 2017. Available from: http://www.theses.fr/2017PESC1116


University of Waterloo

29. Chaperon, Julien. A Stochastic Framework for Soil-Structure Interaction and Constitutive Modelling.

Degree: 2019, University of Waterloo

 A stochastic framework for soil-structure interaction and constitutive modelling is investigated in this thesis and developed to account for uncertainties in material properties and loading… (more)

Subjects/Keywords: soil; soil-structure interaction; foundation; uncertainty; random field; stochastic process; constitutive law; Finite Element Method; Stochastic Finite Element Method; Karhunen-Loeve expansion; Polynomial Chaos Expansion; Fokker-Planck-Kolmogorov equation; linear elastic; linear elastic-plastic

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

APA (6th Edition):

Chaperon, J. (2019). A Stochastic Framework for Soil-Structure Interaction and Constitutive Modelling. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14901

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):

Chaperon, Julien. “A Stochastic Framework for Soil-Structure Interaction and Constitutive Modelling.” 2019. Thesis, University of Waterloo. Accessed March 09, 2021. http://hdl.handle.net/10012/14901.

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

MLA Handbook (7th Edition):

Chaperon, Julien. “A Stochastic Framework for Soil-Structure Interaction and Constitutive Modelling.” 2019. Web. 09 Mar 2021.

Vancouver:

Chaperon J. A Stochastic Framework for Soil-Structure Interaction and Constitutive Modelling. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2021 Mar 09]. Available from: http://hdl.handle.net/10012/14901.

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

Council of Science Editors:

Chaperon J. A Stochastic Framework for Soil-Structure Interaction and Constitutive Modelling. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14901

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

30. Bourgey, Florian. Stochastic approximations for financial risk computations : Approximations stochastiques pour les calculs de risques financiers.

Degree: Docteur es, Mathématiques appliquées, 2020, Institut polytechnique de Paris

Dans cette thèse, nous examinons plusieurs méthodes d'approximations stochastiques à la fois pour le calcul de mesures de risques financiers et pour le pricing de… (more)

Subjects/Keywords: Monte Carlo multi-Niveaux; Marge Initiale; Risk management; Mesures de risque de crédit; Métamodélisation; Décomposition en polynômes du chaos; Polynômes orthogonaux; Options américaines; VIX; Approximation faibles; Variance Forward; Multilevel Monte Carlo; Initial Margin; Risk management; Credit risk measures; Meta-modeling; Polynomial Chaos Expansion; Orthogonal polynomials; American options; VIX; Weak approximations; Forward Variance; 658.150 151

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

APA (6th Edition):

Bourgey, F. (2020). Stochastic approximations for financial risk computations : Approximations stochastiques pour les calculs de risques financiers. (Doctoral Dissertation). Institut polytechnique de Paris. Retrieved from http://www.theses.fr/2020IPPAX052

Chicago Manual of Style (16th Edition):

Bourgey, Florian. “Stochastic approximations for financial risk computations : Approximations stochastiques pour les calculs de risques financiers.” 2020. Doctoral Dissertation, Institut polytechnique de Paris. Accessed March 09, 2021. http://www.theses.fr/2020IPPAX052.

MLA Handbook (7th Edition):

Bourgey, Florian. “Stochastic approximations for financial risk computations : Approximations stochastiques pour les calculs de risques financiers.” 2020. Web. 09 Mar 2021.

Vancouver:

Bourgey F. Stochastic approximations for financial risk computations : Approximations stochastiques pour les calculs de risques financiers. [Internet] [Doctoral dissertation]. Institut polytechnique de Paris; 2020. [cited 2021 Mar 09]. Available from: http://www.theses.fr/2020IPPAX052.

Council of Science Editors:

Bourgey F. Stochastic approximations for financial risk computations : Approximations stochastiques pour les calculs de risques financiers. [Doctoral Dissertation]. Institut polytechnique de Paris; 2020. Available from: http://www.theses.fr/2020IPPAX052

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