Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models
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Publication:2124564
DOI10.1016/j.jcp.2020.109604OpenAlexW2939553101WikidataQ114163501 ScholiaQ114163501MaRDI QIDQ2124564
David A. Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
Publication date: 11 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1904.08069
Monte Carlomachine learninguncertainty quantificationuncertainty reductionpolynomial chaosconditional Karhunen-Loève expansion
Related Items (5)
Physics-informed machine learning with conditional Karhunen-Loève expansions ⋮ Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems ⋮ Active learning based sampling for high-dimensional nonlinear partial differential equations ⋮ Conditional Karhunen-Loève regression model with basis adaptation for high-dimensional problems: uncertainty quantification and inverse modeling ⋮ Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations
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