Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling

Author:

Yeung Yu-HongORCID,Tipireddy Ramakrishna,Barajas-Solano David A.ORCID,Tartakovsky Alexandre M.ORCID

Funder

U.S. Department of Energy

Advanced Scientific Computing Research

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference33 articles.

1. Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs;Yang,2019

2. Physics-informed neural network method for forward and backward advection–dispersion equations;He;Water Resour. Res.,2021

3. Physics-informed machine learning method for large-scale data assimilation problems;Yeung;Water Resour. Res.,2022

4. Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems;Yeung,2023

5. Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons;Psaros;J. Comput. Phys.,2023

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