用于预测光学性质及超材料自由形式逆向设计的深度神经网络
Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials
AI总结：
本文提出用深度神经网络预测超表面等纳米结构的光学性质，并完成可纳入实验可行性约束的自由形式逆向设计，为基于波动方程的技术自动化设计提供新途径。
摘要：
物理学中的许多现象，包括光、水波和声音，都由波动方程描述。给定系数后，波动方程可以被高精度求解，但波长尺度的存在往往意味着，除最简单几何结构之外的任何问题都需要大规模计算机模拟。逆向问题，即根据边界上的场确定系数，要求更高，因为传统优化需要依次求解大量正问题。在此，我们展示波动方程的自由形式逆向问题可以通过机器学习解决。首先，我们表明深度神经网络可用于预测纳米结构材料（如超表面）的光学性质。随后，我们演示了此类纳米结构的自由形式逆向设计，并表明可以将实验可行性所施加的约束纳入考虑。我们的神经网络有望在多种基于波动方程的技术中实现自动化设计。
英文摘要：
Many phenomena in physics, including light, water waves, and sound, are described by wave equations. Given their coefficients, wave equations can be solved to high accuracy, but the presence of the wavelength scale often leads to large computer simulations for anything beyond the simplest geometries. The inverse problem, determining the coefficients from a field on a boundary, is even more demanding, since traditional optimization requires a large number of forward problems be solved sequentially. Here we show that the free-form inverse problem of wave equations can be solved with machine learning. First we show that deep neural networks can be used to predict the optical properties of nanostructured materials such as metasurfaces. Then we demonstrate the free-form inverse design of such nanostructures and show that constraints imposed by experimental feasibility can be taken into account. Our neural networks promise automated design in several technologies based on the wave equation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
