Work

JefiAtten: an attention-based neural network model for solving Maxwell’s equations with charge and current sources

Year: 2024

Type: article

Abstract: We present JefiAtten, a novel neural network model employing the attention mechanism to solve Maxwell’s equations efficiently. JefiAtten uses self-attention and cross-attention modules to understand the interplay between charge density, current density, and electromagnetic fields. Our results indicate that JefiAtten can generalize well to a range of scenarios, maintaining accuracy across various spatial distribution and handling amplitude variations. The model showcases an improvement in computation speed after training, compared to traditional integral methods. The adaptability of the model suggests potential for broader applications in computational physics, with further refinements to enhance its predictive capabilities and computational efficiency. Our work is a testament to the efficacy of integrating attention mechanisms with numerical simulations, marking a step forward in the quest for data-driven solutions to physical phenomena.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ad6ee9

Authors: Ming-Yan Sun, Peng Xu, Jun-Jie Zhang, Tai-Jiao Du, Jian-Guo Wang

Institutions: Northwest Institute of Nuclear Technology, Xi'an Jiaotong University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,610

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China