Inverse design of structural color: finding multiple solutions via conditional generative adversarial networks.

ABSTRACT: The "one-to-many" problem is a typical challenge that faced by many machine learning aided inverse nanophotonics designs where one target optical response can be achieved by many solutions (designs). Although novel training approaches, such as tandem network, and network architecture, such as the mixture density model, have been proposed, the critical problem of solution degeneracy still exists where some possible solutions or solution spaces are discarded or unreachable during the network training process. Here, we report a solution to the "one-to-many" problem by employing a conditional generative adversarial network (cGAN) that enables generating sets of multiple solution groups to a design problem. Using the inverse design of a transmissive Fabry-Pérot-cavity-based color filter as an e

SUBMITTER: Dai P 

PROVIDER: S-EPMC11501759 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature