R-PINN: Recovery-type a-posteriori estimator enhanced adaptive PINN

TL;DR

This paper introduces R-PINN, an adaptive physics-informed neural network that uses a recovery-type a-posteriori error estimator to improve accuracy and convergence in solving PDEs with large local gradients.

Contribution

The paper proposes R-PINN, a novel hybrid adaptive PINN method that incorporates a recovery-type a-posteriori estimator inspired by FEM to enhance solution accuracy.

Findings

R-PINN converges faster with fewer adaptive points.

R-PINN outperforms FI-PINN in regions with large errors.

The method effectively handles PDEs with multiple high-gradient regions.

Abstract

In recent years, with the advancements in machine learning and neural networks, algorithms using physics-informed neural networks (PINNs) to solve PDEs have gained widespread applications. While these algorithms are well-suited for a wide range of equations, they often exhibit suboptimal performance when applied to equations with large local gradients, resulting in substantial localized errors. To address this issue, this paper proposes an adaptive PINN algorithm designed to improve accuracy in such cases. The core idea of the algorithm is to adaptively adjust the distribution of collocation points based on the recovery-type a-posterior error of the current numerical solution, enabling a better approximation of the true solution. This approach is inspired by the adaptive finite element method. By combining the recovery-type a-posteriori estimator, a gradient-recovery estimator commonly used in the adaptive finite element method (FEM) with PINNs, we introduce the Recovery-type a-posteriori estimator enhanced adaptive PINN (R-PINN) and compare its performance with a typical adaptive PINN algorithm, FI-PINN. Our results demonstrate that R-PINN achieves faster convergence with fewer adaptive points and significantly outperforms in the cases with multiple regions of large errors than FI-PINN. Notably, our method is a hybrid numerical approach for solving partial differential equations, integrating adaptive FEM with PINNs.