A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities
Computational Physics
2020-06-08 v1
Abstract
We develop a novel computational method for evaluating the extreme excursion probabilities arising from random initialization of nonlinear dynamical systems. The method uses excursion probability theory to formulate a sequence of Bayesian inverse problems that, when solved, yields the biasing distribution. Solving multiple Bayesian inverse problems can be expensive; more so in higher dimensions. To alleviate the computational cost, we build machine-learning-based surrogates to solve the Bayesian inverse problems that give rise to the biasing distribution. This biasing distribution can then be used in an importance sampling procedure to estimate the extreme excursion probabilities.
Cite
@article{arxiv.2006.03466,
title = {A Machine-Learning-Based Importance Sampling Method to Compute Rare Event Probabilities},
author = {Vishwas Rao and Romit Maulik and Emil Constantinescu and Mihai Anitescu},
journal= {arXiv preprint arXiv:2006.03466},
year = {2020}
}
Comments
arXiv admin note: text overlap with arXiv:2001.11904