Work

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

Year: 2024

Type: article

Source: SIAM Journal on Scientific Computing

DOI: 10.1137/23m1546981

Authors: Tiangang Cui, Sergey Dolgov, Robert Scheichl

Institutions: The University of Sydney, University of Bath, Heidelberg University

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: closed

Funders: Deutsche Forschungsgemeinschaft, Erwin Schrödinger International Institute for Mathematics and Physics, Engineering and Physical Sciences Research Council, Australian Research Council

Awards: Tensor decomposition sampling algorithms for Bayesian inverse problems, EXC 2181: STRUKTUREN: Emergenz in Natur, Mathematik und komplexen Daten, G7141960837, Interface-aware numerical methods for stochastic inverse problems

OpenAlex ID: W4391173633

FWCI: 1.653

Cites: 55

Cited by: 5

Related to: 10