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

DOI10.1016/j.camwa.2023.07.028arXiv2111.12408OpenAlexW4385860834MaRDI QIDQ6052371

Nikolaj T. Mücke, Cornelis W. Oosterlee, B. Sanderse, Sander M. Bohte

Publication date: 21 September 2023

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.12408

zbMATH Keywords

inverse problemsMarkov chain Monte CarloBayesian inferencegenerative adversarial networks

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Automated solution of differential equations by the finite element method. The FEniCS book
• Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems
• Inverse acoustic and electromagnetic scattering theory.
• Non-intrusive reduced order modeling of nonlinear problems using neural networks
• Ensemble Kalman inversion: mean-field limit and convergence analysis
• Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
• A multigrid multilevel Monte Carlo method for transport in the Darcy-Stokes system
• Inverse problems: A Bayesian perspective
• Superconvergent discontinuous Galerkin methods for second-order elliptic problems
• Machine Learning for Fluid Mechanics
• Handbook of Markov Chain Monte Carlo
• Data-Driven Computational Methods
• Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
• On the local Lipschitz stability of Bayesian inverse problems
• Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions
• Equation of State Calculations by Fast Computing Machines
• An Invitation to Statistics in Wasserstein Space
• Transform-based particle filtering for elliptic Bayesian inverse problems
• Data Assimilation
• Monte Carlo sampling methods using Markov chains and their applications
• Probability theory and stochastic processes