Enriched Physics-informed Neural Networks for Dynamic Poisson-Nernst-Planck Systems

TL;DR

This paper introduces an enriched physics-informed neural network (EPINNs) that effectively solves complex, coupled Poisson-Nernst-Planck equations with improved accuracy, stability, and speed over traditional methods, utilizing adaptive loss weighting, resampling, and GPU acceleration.

Contribution

The paper develops EPINNs with adaptive loss weights, resampling, and GPU acceleration to efficiently solve dynamic PNP systems, outperforming traditional physics-informed neural networks.

Findings

EPINNs demonstrate higher accuracy than traditional PINNs.

EPINNs show better stability and faster convergence.

The method effectively handles arbitrary boundary conditions.

Abstract

This paper proposes a meshless deep learning algorithm, enriched physics-informed neural networks (EPINNs), to solve dynamic Poisson-Nernst-Planck (PNP) equations with strong coupling and nonlinear characteristics. The EPINNs takes the traditional physics-informed neural networks as the foundation framework, and adds the adaptive loss weight to balance the loss functions, which automatically assigns the weights of losses by updating the parameters in each iteration based on the maximum likelihood estimate. The resampling strategy is employed in the EPINNs to accelerate the convergence of loss function. Meanwhile, the GPU parallel computing technique is adopted to accelerate the solving process. Four examples are provided to demonstrate the validity and effectiveness of the proposed method. Numerical results indicate that the new method has better applicability than traditional numerical methods in solving such coupled nonlinear systems. More importantly, the EPINNs is more accurate, stable, and fast than the traditional physics-informed neural networks. This work provides a simple and high-performance numerical tool for addressing PNPs with arbitrary boundary shapes and boundary conditions.