Convergence and error control of consistent PINNs for elliptic PDEs

TL;DR

This paper analyzes the convergence and error control of consistent PINNs for elliptic PDEs, proposing a new loss function that guarantees optimal recovery of solutions under certain conditions.

Contribution

It introduces a new data-driven loss function for PINNs that ensures optimal error bounds and analyzes its effectiveness for elliptic boundary value problems.

Findings

The new loss function achieves optimal recovery error for elliptic PDE solutions.

PINNs with the proposed loss function provide guaranteed error bounds under model class assumptions.

Numerical examples demonstrate the benefits of the proposed loss functions.

Abstract

We provide an a priori analysis of collocation methods for solving elliptic boundary value problems. They begin with information in the form of point values of the data and utilize only this information to numerically approximate the solution u of the PDE. For such a method to provide an approximation with guaranteed error bounds, additional assumptions on the data, called model class assumptions, are needed. We determine the best error of approximating u in the energy norm, in terms of the total number of point samples, under all Besov class model assumptions for the right hand side and boundary data. We then turn to the study of numerical procedures and analyze whether a proposed numerical procedure achieves the optimal recovery error. We analyze numerical methods which generate the numerical approximation to $u$ by minimizing specified data driven loss functions over a set $\Sigma$ which is either a finite dimensional linear space, or more generally, a finite dimensional manifold. We show that the success of such a procedure depends critically on choosing a data driven loss function that is consistent with the PDE and provides sharp error control. Based on this analysis a new loss function is proposed. We also address the recent methods of Physics Informed Neural Networks. We prove that minimization of the new loss over restricted neural network spaces $\Sigma$ provides an optimal recovery of the solution $u$, provided that the optimization problem can be numerically executed and $\Sigma$ has sufficient approximation capabilities. We also analyze variants of the new loss function which are more practical for implementation. Finally, numerical examples illustrating the benefits of the proposed loss functions are given.