Open Access

ARTICLE

A Generative Residual Enhanced Neural Operator Based on the Boundary Element Method for Accurate Metasurface Parameter Analysis

Huilan Wu, Yijun Liu^*

Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, China

* Corresponding Author: Yijun Liu. Email:

(This article belongs to the Special Issue: AI-Enhanced Computational Mechanics and Structural Optimization Methods)

Computer Modeling in Engineering & Sciences 2026, 147(2), 14 https://doi.org/10.32604/cmes.2026.081675

Received 06 March 2026; Accepted 02 May 2026; Issue published 27 May 2026

Abstract

Metasurface design often requires solving field distributions across varying structural parameters and frequencies, where neural operators offer a promising avenue for fast prediction. However, conventional neural operators have problems with degradation of the accuracy in multi-scale structural analysis. In this work, we propose a Generative Residual Enhanced Neural Operator (GRE-NO) framework that introduces a generative residual network to model the systematic bias of the main predictor. The core model retains the DeepONet architecture with both branch and trunk networks implemented using Fourier Neural Operators, combining strong generalization and efficient global representation. To handle the complexity of unbounded acoustic scattering problems, we integrate the Boundary Element Method (BEM) into data modeling and field computation, which reduces the problem dimensionality and enables training with samples at the 104 scale. Numerical experiments on some 2D and 3D acoustic metasurface problems demonstrate that the developed GRE-NO achieves excellent accuracy in results with relative errors under 1% in this study, outperforming conventional neural networks in accuracy of prediction.

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Keywords

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