发表机构
University of California, Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出动态生成训练样本的算法，结合源与真实值归一化及演化重放数据集，训练出可归纳扩展的单步神经代理模型，在波散射逆问题中实现超73.8倍速度提升，性能优于FDTD相关设计。
AI 中文摘要
神经网络代理模型是有限差分时域（FDTD）等传统电磁波模拟器的新兴替代方案，其目标是用预训练的神经网络替代严谨的物理模拟，以解决波散射正问题和逆问题，速度提升数个数量级。然而，非循环单步代理模型仅能扩展到数十个模拟变量。本文表明，通过在训练过程中动态生成显著训练样本（而非从庞大的可能样本空间中随机采样）可克服这一障碍。我们引入一种与代理模型训练并行运行的算法，利用梯度上升法搜索折射率和源配置，寻找代理模型与全波真实模拟器不一致的情况；还采用源和真实值归一化，结合不断演化的重放数据集以稳定并加速学习。通过该方法，我们训练了一个快速单步代理模型，用于二维波散射，其可控变量多达41772个，包括密集、可自由配置的折射率网格和复值源。所得神经代理模型在多样的结构化和非结构化样本中具有稳健的准确性，且可归纳泛化到更大的域，无需重新训练即可扩展至超过300万个可控变量，实现了73.8倍的提升。我们在大规模正模拟以及宽度达98个波长的自由形分束器和梯度折射率（GRIN）透镜的逆设计中验证了该代理模型，其性能与基于FDTD的设计相当或更优，速度提升幅度为1.29倍至26.5倍。这些结果为光子逆设计及其他波散射逆问题的快速、稳健准确且可归纳扩展的神经模拟器提供了可行路径。
英文摘要
Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace rigorous physical simulations with pre-trained neural networks that solve wave-scattering forward and inverse problems orders of magnitude faster. However, nonrecurrent, single-step surrogates have scaled only to a few tens of simulation variables. Here, we show that this barrier can be overcome by dynamically generating salient training examples during training, rather than randomly sampling the large space of possible examples. We introduce an algorithm that runs in parallel with surrogate training, using gradient ascent to search refractive-index and source configurations for cases where the surrogate disagrees with a full-wave ground-truth simulator. We also use source and ground-truth normalization with an evolving replay dataset to stabilize and accelerate learning. Using this approach, we train a fast, single-step surrogate for two-dimensional wave scattering with up to 41,772 controllable variables, including dense, freely configurable grids of refractive indices and complex-valued sources. The resulting neural surrogate is robustly accurate across diverse structured and unstructured examples and generalizes inductively to larger domains, reaching over 3 million controllable variables without retraining, a $73.8\times$ increase. We demonstrate the surrogate on large-scale forward simulations and inverse design of freeform beam splitters and gradient-index (GRIN) lenses up to 98 wavelengths wide, showing comparable or better performance than FDTD-based designs, with speedups from $1.29\times$ to $26.5\times$. These results demonstrate a practical path toward fast, robustly accurate, inductively scalable neural simulators for photonic inverse design and other wave-scattering inverse problems.
