Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques.

翻译：神经算子已成为科学机器学习中求解偏微分方程的有力工具。本文实现并训练了一种改进的傅里叶神经算子，作为电磁散射问题的代理求解器，并将其数据效率与现有方法进行了比较。我们进一步展示了其在自由形态、全三维电磁散射体梯度纳米光子逆向设计中的应用，这一领域此前尚未有深度学习技术的成功应用。