TY - EJOU AU - Wu, Huilan AU - Liu, Yijun TI - A Generative Residual Enhanced Neural Operator Based on the Boundary Element Method for Accurate Metasurface Parameter Analysis T2 - Computer Modeling in Engineering \& Sciences PY - 2026 VL - 147 IS - 2 SN - 1526-1506 AB - 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. KW - Generative residual network; Fourier neural operator; DeepONet; boundary element method; cross-scale metasurface analysis DO - 10.32604/cmes.2026.081675