End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network.

ABSTRACT: Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a wide

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10534592 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature