Coupling the reduced-order model and the generative model for an importance sampling estimator

DOI10.1016/j.jcp.2020.109281OpenAlexW3003490334MaRDI QIDQ2123351

Shuangqing Wei, Xiaoliang Wan

Publication date: 8 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.07977

zbMATH Keywords

importance samplingmachine learninguncertainty quantificationreduced-order modelinggenerative model

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (3)

Adaptive deep density approximation for Fokker-Planck equations ⋮ VAE-KRnet and Its Applications to Variational Bayes ⋮ Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events

Uses Software

• Adam
• NICE
• Glow
• Flow-GAN

Cites Work

• Spectral polynomial chaos solutions of the stochastic advection equation
• Boosting with early stopping: convergence and consistency
• The Wick--Malliavin Approximation of Elliptic Problems with Log-Normal Random Coefficients
• Multilevel Monte Carlo Methods
• Multivariate Density Estimation

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