Diffusion probabilistic model based accurate and high-degree-of-freedom metasurface inverse design.

ABSTRACT: Conventional meta-atom designs rely heavily on researchers' prior knowledge and trial-and-error searches using full-wave simulations, resulting in time-consuming and inefficient processes. Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by generative adversarial networks (GANs) have been applied to inverse design of metamaterials, which can directly generate high-degree-of-freedom meta-atoms based on S-parameters requirements. However, the adversarial training process of GANs makes the network unstable and results in high modeling costs. This p

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC11501780 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature