Solving Maxwell's Equation in 2D with Neural Networks with Local Converging Inputs

TL;DR

This paper introduces a neural network method with local converging inputs to efficiently solve 2D Maxwell's equations around perfect electric conductors, significantly reducing computational costs while maintaining accuracy.

Contribution

The paper presents a novel neural network approach leveraging local patches of numerical solutions, improving efficiency and robustness in solving Maxwell's equations around PECs.

Findings

Achieves an 8^3 reduction in computational complexity.

Successfully generalizes to different PEC geometries.

Provides accurate solutions with local patch inputs.

Abstract

In this paper we apply neural networks with local converging inputs (NNLCI), originally introduced in [arXiv:2109.09316], to solve the two dimensional Maxwell's equation around perfect electric conductors (PECs). The input to the networks consist of local patches of low cost numerical solutions to the equation computed on two coarse grids, and the output is a more accurate solution at the center of the local patch. We apply the recently developed second order finite difference method [arXiv:2209.00740] to generate the input and training data which captures the scattering of electromagnetic waves off of a PEC at a given terminal time. The advantage of NNLCI is that once trained it offers an efficient alternative to costly high-resolution conventional numerical methods; our numerical experiments indicate the computational complexity saving by a factor of $8^3$ in terms of the number of spatial-temporal grid points. In contrast with existing research work on applying neural networks to directly solve PDEs, our method takes advantage of the local domain of dependence of the Maxwell's equation in the input solution patches, and is therefore simpler, yet still robust. We demonstrate that we can train our neural network on some PECs to predict accurate solutions to different PECs with quite different geometries from any of the training examples.