Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications-Reference-Cited by-同舟云学术

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Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications

Published:2023-10 Issue: Volume:147 Page:278-299
ISSN:0898-1221
Container-title:Computers & Mathematics with Applications
language:en
Short-container-title:Computers & Mathematics with Applications

Author:

Mücke Nikolaj T.^ORCID,Sanderse Benjamin,Bohté Sander M.,Oosterlee Cornelis W.

Publisher

Elsevier BV

Subject

Computational Mathematics,Computational Theory and Mathematics,Modeling and Simulation

Reference58 articles.

1. Data Assimilation: Methods, Algorithms, and Applications;Asch,2016

2. Data-Driven Computational Methods: Parameter and Operator Estimations;Harlim,2018

3. Inverse problems: a Bayesian perspective;Stuart;Acta Numer.,2010

4. Statistical and Computational Inverse Problems, vol. 160;Kaipio,2006

5. A probabilistic graphical model foundation for enabling predictive digital twins at scale;Kapteyn;Nat. Comput. Sci.,2021

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1. The deep latent space particle filter for real-time data assimilation with uncertainty quantification;Scientific Reports;2024-08-21

2. Adaptive event‐triggered secure control for networked control systems subject to deception and replay cyber‐attacks;International Journal of Adaptive Control and Signal Processing;2024-05-27

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