GAUSSIAN PROCESS REGRESSION AND CONDITIONAL KARHUNEN-LOÈVE EXPANSION FOR FORWARD UNCERTAINTY QUANTIFICATION AND INVERSE MODELING IN THE PRESENCE OF MEASUREMENT NOISE-Reference-Cited by-同舟云学术

GAUSSIAN PROCESS REGRESSION AND CONDITIONAL KARHUNEN-LOÈVE EXPANSION FOR FORWARD UNCERTAINTY QUANTIFICATION AND INVERSE MODELING IN THE PRESENCE OF MEASUREMENT NOISE

Published:2022 Issue:2 Volume:3 Page:71-86
ISSN:2689-3967
Container-title:Journal of Machine Learning for Modeling and Computing
language:en
Short-container-title:J Mach Learn Model Comput

Author:

Li Jing,Tartakovsky Alexandre M.

Publisher

Begell House

Subject

General Medicine

Link

https://www.dl.begellhouse.com/download/article/04b58db6406bafe0/(4)JMLMC0302-41893.pdf

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3. Doostan, A., Validi, A., and Iaccarino, G., Non-Intrusive Low-Rank Separated Approximation of High-Dimensional Stochastic Models, Comput. Methods Appl. Mech. Eng., vol. 263, pp. 42-55,2013.

4. Gorodetsky, A.A. and Jakeman, J.D., Gradient-Based Optimization for Regression in the Functional Tensor-Train Format, J. Comput. Phys, vol. 374, pp. 1219-1238,2018.

5. Hampton, J. and Doostan, A., Compressive Sampling ofPolynomial Chaos Expansions: Convergence Analysis and Sampling Strategies, J. Comput. Phys, vol. 280, pp. 363-386,2015.

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