基于深度学习模型的电磁超表面设计关键技术OA

[Objective]To address the issue that deep-learning-based metasurface optimization relies heavi-ly on large-scale full-wave simulation data,thereby limiting optimization efficiency,this study investigates dataset construction methods,deep learning model architecture design methods,deep learning model training strategies,and optimization approaches for metasurface structure design scenarios.[Method]First,a training-set sampling method based on sample importance is developed.By evaluating the gradient information of the loss function with respect to individual samples,this method strategically identifies and selects highly informa-tive data points,significantly reducing the required sample size while improving the accuracy of electromag-netic response prediction for metasurface structures.Second,a multimodal deep learning model is constructed to simultaneously extract and integrate features from both vectorized structural parameters and pixelated pat-tern representations.Through a systematic feature fusion mechanism,the model enhances structural represen-tation capability and further improves electromagnetic response prediction performance.Third,a novel train-ing strategy that exploits the out-of-distribution(OOD)generalization capability of the deep learning model is proposed.This strategy leverages the intrinsic generalization ability of the model to synthesize and incorpo-rate low-fidelity response samples outside the original training distribution,dynamically expanding the feature space and thereby reducing the required scale of the high-fidelity training dataset.Finally,instead of relying on a globally accurate model that incurs substantial computational training costs,an efficient optimization method for metasurface structure design is proposed.This approach employs a coarse deep learning model trained on a strictly limited dataset and combines it with an iterative refinement mechanism to guide the optimization pro-cess.[Results]Numerical results demonstrate the high efficiency of the proposed data-driven framework.Specifically,while rigorously maintaining the baseline performance of the deep learning models,the proposed dataset construction method,multimodal model architecture,and OOD-based training strategy each reduce the number of full-wave simulation samples required for initial training by 30%-50%.This substantial reduction significantly alleviates the computational burden associated with generating high-fidelity datasets.Further-more,during the practical optimization stage,the proposed optimization algorithm based on a coarse deep learning model is shown to achieve rapid convergence.Metasurface structures with excellent electromagnetic performance can be successfully designed and synthesized using only dozens of additional iterative updates and full-wave simulation validations.These results demonstrate the capability of the proposed method to elimi-nate the dependence on highly accurate yet computationally expensive surrogate models.[Conclusion]A low-data-dependency system framework covering the complete process of"data-model-training-application"is established.Through targeted restructuring at four different levels,it systematically addresses the challenge of limited data scale that constrains the efficiency of metasurface design.As a result,it provides a general methodological foundation and practical paradigm for the low-cost application of deep learning techniques in the field of electromagnetic engineering.