An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks

DOI10.4208/jcm.2102-m2020-0339zbMath1499.62346arXiv2104.06285OpenAlexW3209989675MaRDI QIDQ5079536

Tao Zhou, Liang Yan

Publication date: 27 May 2022

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

Full work available at URL: https://arxiv.org/abs/2104.06285

zbMATH Keywords

Markov chain Monte CarloBayesian inverse problemsdeep neural network

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Probabilistic models, generic numerical methods in probability and statistics (65C20) Monte Carlo methods (65C05) Inverse problems for PDEs (35R30) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (1)

Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks

Uses Software

• RMSprop
• Adam
• ADADELTA
• PRMLT

Cites Work

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