论文标题
深度神经网络，用于预测光学特性和超材料的自由形式逆设计
Deep neural networks for the prediction of the optical properties and the free-form inverse design of metamaterials
论文作者
论文摘要
波动方程描述了物理学中的许多现象，包括光，水波和声音。鉴于它们的系数，波动方程可以求解至高精度，但是波长刻度的存在通常会导致大型计算机模拟，但除了最简单的几何形状以外的任何事物。逆问题是，从边界上的字段确定系数的要求更高，因为传统优化需要依次解决大量的正向问题。在这里，我们表明，可以通过机器学习来解决波动方程的自由形式的反问题。首先，我们表明，深层神经网络可用于预测纳米结构材料（例如metasurfaces）的光学特性。然后，我们证明了此类纳米结构的自由形式的逆设计，并表明可以考虑实验可行性施加的约束。我们的神经网络承诺基于波动方程的几种技术自动设计。
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.
