Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators

TL;DR

This paper demonstrates that physics-informed deep operator networks (PI-DeepONets) effectively solve forward and inverse PDE problems on unknown manifolds using data-driven manifold identification and operator approximation schemes, achieving high accuracy with limited data.

Contribution

The paper introduces a novel framework combining DeepONets with physics-informed loss functions and manifold approximation methods for PDEs on unknown manifolds, including theoretical error bounds and Bayesian inverse problem solutions.

Findings

PI-DeepONets outperform standard DeepONets with limited data.

Accurate PDE solutions for various diffusion coefficients.

Effective inverse coefficient estimation via Bayesian MCMC.

Abstract

In this paper, we evaluate the effectiveness of deep operator networks (DeepONets) in solving both forward and inverse problems of partial differential equations (PDEs) on unknown manifolds. By unknown manifolds, we identify the manifold by a set of randomly sampled data point clouds that are assumed to lie on or close to the manifold. When the loss function incorporates the physics, resulting in the so-called physics-informed DeepONets (PI-DeepONets), we approximate the differentiation terms in the PDE by an appropriate operator approximation scheme. For the second-order elliptic PDE with a nontrivial diffusion coefficient, we approximate the differentiation term with one of these methods: the Diffusion Maps (DM), the Radial Basis Functions (RBF), and the Generalized Moving Least Squares (GMLS) methods. For the GMLS approximation, which is more flexible for problems with boundary conditions, we derive the theoretical error bound induced by the approximate differentiation. Numerically, we found that DeepONet is accurate for various types of diffusion coefficients, including linear, exponential, piecewise linear, and quadratic functions, for linear and semi-linear PDEs with/without boundaries. When the number of observations is small, PI-DeepONet trained with sufficiently large samples of PDE constraints produces more accurate approximations than DeepONet. For the inverse problem, we incorporate PI-DeepONet in a Bayesian Markov Chain Monte Carlo (MCMC) framework to estimate the diffusion coefficient from noisy solutions of the PDEs measured at a finite number of point cloud data. Numerically, we found that PI-DeepONet provides accurate approximations comparable to those obtained by a more expensive method that directly solves the PDE on the proposed diffusion coefficient in each MCMC iteration.