Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response.
物理光学大作业，利用全连接层神经网络、残差连接网络、卷积神经网络学习光学结构与光谱响应的内在联系，可以快速的实现较准确的光谱预测。同时也可以训练神经网络实现从光谱响应到光学结构参数的过程。