@inproceedings{
dasgupta2023conditional,
title={Conditional score-based generative models for solving physics-based inverse problems},
author={Agnimitra Dasgupta and Javier Murgoitio-Esandi and Deep Ray and Assad Oberai},
booktitle={NeurIPS 2023 Workshop on Deep Learning and Inverse Problems},
year={2023},
url={https://openreview.net/forum?id=ZL5wlFMg0Y}
}
We propose to sample from high-dimensional posterior distributions arising in physics-based inverse problems using conditional score-based generative models. The proposed approach trains a noise-conditional score network to approximate the score function of the posterior distribution. Then, the network is used to sample from the posterior distribution through annealed Langevin dynamics. The proposed method is applicable even when we can only simulate the forward problem. We apply it to two physics-based inverse problems and compare its performance with conditional generative adversarial networks. Results show that conditional score-based generative models can reliably perform Bayesian inference.
Conditional score-based generative models for solving physics-based inverse problems
Agnimitra Dasgupta Javier Murgoitio-Esandi Deep Ray Assad Oberai