Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs

no code implementations • NeurIPS Workshop Deep_Invers 2021 • Deep Ray, Dhruv V Patel, Harisankar Ramaswamy, Assad Oberai

In this work, we propose a conditional generative adversarial network (cGAN) to sample from the posterior of physics-based Bayesian inference problems.

Bayesian Inference Generative Adversarial Network

Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors

no code implementations • 6 Jul 2021 • Dhruv V Patel, Deep Ray, Assad A Oberai

Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems.

Bayesian Inference Generative Adversarial Network +1

Bayesian Inference in Physics-Driven Problems with Adversarial Priors

no code implementations • 23 Oct 2020 • Dhruv V Patel, Deep Ray, Harisankar Ramaswamy, Assad Oberai

Generative adversarial networks (GANs) have found multiple applications in the solution of inverse problems in science and engineering.

Bayesian Inference