Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise

TL;DR

This paper introduces a robust multi-scale BPINN method that improves convergence and reduces computational costs for solving noisy, multi-scale elliptic PDEs, outperforming traditional HMC-based approaches.

Contribution

The paper develops a novel MBPINN framework integrating MscaleDNN and reframe HMC with SGD, enhancing robustness and efficiency in solving complex PDEs with noise.

Findings

MBPINN avoids HMC failures in noisy PDE problems.

It produces accurate solutions for multi-scale elliptic PDEs.

The method is computationally more efficient than HMC.

Abstract

Bayesian Physics Informed Neural Networks (BPINN) have attracted considerable attention for inferring the system states and physical parameters of differential equations according to noisy observations. However, in practice, Hamiltonian Monte Carlo (HMC) used to estimate the internal parameters of the solver for BPINN often encounters these troubles including poor performance and awful convergence for a given step size used to adjust the momentum of those parameters. To address the convergence of HMC for the BPINN method and extend its application scope to multi-scale partial differential equations (PDE), we develop a robust multi-scale BPINN (dubbed MBPINN) method by integrating multi-scale deep neural networks (MscaleDNN) and the BPINN framework. In this newly proposed MBPINN method, we reframe HMC with Stochastic Gradient Descent (SGD) to ensure the most ``likely'' estimation is always provided, and we configure its solver as a Fourier feature mapping-induced MscaleDNN. This novel method offers several key advantages: (1) it is more robust than HMC, (2) it incurs less computational cost than HMC, and (3) it is more flexible for complex problems. We demonstrate the applicability and performance of the proposed method through some general Poisson and multi-scale elliptic problems in one and two-dimensional Euclidean spaces. Our findings indicate that the proposed method can avoid HMC failures and provide valid results. Additionally, our method is capable of handling complex elliptic PDE and producing comparable results for general elliptic PDE under the case of lower signal-to-noise rate. These findings suggest that our proposed approach has great potential for physics-informed machine learning for parameter estimation and solution recovery in the case of ill-posed problems.