# Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications

@article{Mcke2021MarkovCG, title={Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications}, author={Nikolaj Takata M{\"u}cke and Benjamin Sanderse and Sander M. Boht'e and Cornelis W. Oosterlee}, journal={ArXiv}, year={2021}, volume={abs/2111.12408} }

In the context of solving inverse problems for physics applications within a Bayesian framework, we present a new approach, Markov Chain Generative Adversarial Neural Networks (MCGANs), to alleviate the computational costs associated with solving the Bayesian inference problem. GANs pose a very suitable framework to aid in the solution of Bayesian inference problems, as they are designed to generate samples from complicated high-dimensional distributions. By training a GAN to sample from a low…

## 2 Citations

### Surrogate Modeling for Bayesian Inverse Problems Based on Physics-Informed Neural Networks

- SSRN Electronic Journal
- 2022

### Generative models and Bayesian inversion using Laplace approximation

- Computer ScienceArXiv
- 2022

This work explores an alternative Bayesian inference based on probabilistic generative models which is carried out in the original high-dimensional space and shows that derived Bayes estimates are consistent, in contrast to the approach employing the low-dimensional manifold of the generative model.

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