Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events-Reference-Cited by-同舟云学术

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Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events

Published:2024-01-24 Issue:1 Volume:46 Page:C1-C29
ISSN:1064-8275
Container-title:SIAM Journal on Scientific Computing
language:en
Short-container-title:SIAM J. Sci. Comput.

Author:

Cui Tiangang¹^ORCID,Dolgov Sergey²^ORCID,Scheichl Robert³^ORCID

Affiliation:

1. School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.

2. Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK.

3. Institute for Mathematics and Interdisciplinary Center for Scientific Computing, Heidelberg University, 69120 Heidelberg, Germany.

Funder

Australian Research Council

Erwin Schrödinger International Institute for Mathematics and Physics

Deutsche Forschungsgemeinschaft

Engineering and Physical Sciences Research Council

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference58 articles.

1. A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

2. Spectral Tensor-Train Decomposition

3. An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting

4. Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space

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