Parameter-Efficient Electromagnetic Surrogate Solver for Broadband Field Prediction using Discrete Wavelength Data

• URL: http://arxiv.org/abs/2408.02971v2
• Date: Tue, 11 Mar 2025 03:02:03 GMT
• Title: Parameter-Efficient Electromagnetic Surrogate Solver for Broadband Field Prediction using Discrete Wavelength Data
• Authors: Joonhyuk Seo, Chanik Kang, Dongjin Seo, Haejun Chung,
• Abstract summary: We propose a broadband surrogate solver capable of providing solutions across a continuous range of wavelengths.<n>Compared to the state-of-the-art surrogate solver, our model achieves an 80.5% improvement in prediction accuracy for untrained wavelengths.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, electromagnetic surrogate solvers, trained on solutions of Maxwell's equations under specific simulation conditions, enabled real-time inference of computationally expensive simulations. However, conventional surrogate solvers often consider only a narrow range of simulation parameters and fail when encountering even slight variations in simulation conditions. To address this limitation, we propose a broadband surrogate solver capable of providing solutions across a continuous range of wavelengths, even when trained on discrete wavelength data. Our approach leverages a Wave-Informed element-wise Multiplicative Encoding and a Fourier Group Convolutional Shuffling operator to mitigate overfitting while capturing the fundamental characteristics of Maxwell's equations. Compared to the state-of-the-art surrogate solver, our model achieves a 74% reduction in parameters, an 80.5% improvement in prediction accuracy for untrained wavelengths, demonstrating superior generalization and efficiency.

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