Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

TL;DR

This paper introduces an adaptive weighting strategy for Bayesian Physics-Informed Neural Networks (BPINNs) that automatically balances multi-objective and multi-scale problems, improving stability, convergence, and uncertainty quantification.

Contribution

The authors develop a novel automatic weighting method for BPINNs that addresses stability and multi-scale challenges, enabling more robust inverse and forward problem solutions.

Findings

Improved convergence and stability of BPINN training.

Enhanced exploration of the Pareto front in multi-objective problems.

Weights reflect task uncertainties and data noise levels.

Abstract

In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible to robustly address multi-objective and multi-scale problems. BPINNs are a popular framework for data assimilation, combining the constraints of Uncertainty Quantification (UQ) and Partial Differential Equation (PDE). The relative weights of the BPINN target distribution terms are directly related to the inherent uncertainty in the respective learning tasks. Yet, they are usually manually set a-priori, that can lead to pathological behavior, stability concerns, and to conflicts between tasks which are obstacles that have deterred the use of BPINNs for inverse problems with multi-scale dynamics. The present weighting strategy automatically tunes the weights by considering the multi-task nature of target posterior distribution. We show that this remedies the failure modes of BPINNs and provides efficient exploration of the optimal Pareto front. This leads to better convergence and stability of BPINN training while reducing sampling bias. The determined weights moreover carry information about task uncertainties, reflecting noise levels in the data and adequacy of the PDE model. We demonstrate this in numerical experiments in Sobolev training, and compare them to analytically $\epsilon$-optimal baseline, and in a multi-scale Lokta-Volterra inverse problem. We eventually apply this framework to an inpainting task and an inverse problem, involving latent field recovery for incompressible flow in complex geometries.