论文标题
通过物理驱动的神经网络预测非线性光学散射
Predicting nonlinear optical scattering with physics-driven neural networks
论文作者
论文摘要
接受身体损失的深度神经网络正在成为非线性数值求解器的有希望的替代物。这些工具可以预测麦克斯韦方程的解决方案以及计算输出场的梯度相对于毫秒毫秒的材料和几何特性，这使它们对逆设计或反向散射应用有吸引力。在这里，我们开发了Maxwellnet的可调版本，Maxwellnet是一个物理驱动的神经网络，能够计算出在光学KERR效应的情况下，具有与入射波长相当的尺寸的光散射。动态调整网络的权重以考虑材料的强度依赖性折射率。
Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and compute gradients of output fields with respect to the material and geometrical properties in millisecond times which makes them attractive for inverse design or inverse scattering applications. Here we develop a tunable version of MaxwellNet, a physics driven neural network able to compute light scattering from inhomogenous media with a size comparable with the incident wavelength in the presence of the optical Kerr effect. The weights of the network are dynamically adjusted to take into account the intensity-dependent refractive index of the material.
