Our work on Generative Adversarial Networks for Bayesian inverse problems just got published in Computers & Mathematics with Applications.

The idea is to train a GAN as a surrogate for a physics model from which one can then easily sample with a Markov chain Monte Carlo technique.

We use this technique for state and parameter estimation in fluid flow models.