Bayesian Physics Informed Neural Networks for Linear Inverse problems

TL;DR

This paper introduces a Bayesian framework for Physics Informed Neural Networks (PINNs) to improve inverse problem solving by incorporating prior knowledge and uncertainty quantification, addressing computational challenges in high-dimensional systems.

Contribution

The paper proposes a novel Bayesian PINN (BPINN) framework that includes deterministic PINNs as a special case and derives posterior laws for neural network parameters for both supervised and unsupervised training.

Findings

Derived posterior probability expressions for unknown variables.

Unified Bayesian framework encompassing deterministic PINNs as MAP estimate.

Discussed implementation challenges in real-world applications.

Abstract

Inverse problems arise almost everywhere in science and engineering where we need to infer on a quantity from indirect observation. The cases of medical, biomedical, and industrial imaging systems are the typical examples. A very high overview of classification of the inverse problems method can be: i) Analytical, ii) Regularization, and iii) Bayesian inference methods. Even if there are straight links between them, we can say that the Bayesian inference based methods are the most powerful, as they give the possibility of accounting for prior knowledge and can account for errors and uncertainties in general. One of the main limitations stay in computational costs in particular for high dimensional imaging systems. Neural Networks (NN), and in particular Deep NNs (DNN), have been considered as a way to push farther this limit. Physics Informed Neural Networks (PINN) concept integrates physical laws with deep learning techniques to enhance the speed, accuracy and efficiency of the above mentioned problems. In this work, a new Bayesian framework for the concept of PINN (BPINN) is presented and discussed which includes the deterministic one if we use the Maximum A Posteriori (MAP) estimation framework. We consider two cases of supervised and unsupervised for training step, obtain the expressions of the posterior probability of the unknown variables, and deduce the posterior laws of the NN's parameters. We also discuss about the challenges of implementation of these methods in real applications.