RIGID uses a random forest forward model and MCMC sampling to generate metamaterial designs satisfying target functional responses, producing broader design-space coverage than genetic algorithms on acoustic and optical test cases with fewer than 250 training samples.
Free-form diffractive metagrating design based on generative adversarial networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.optics 1years
2023 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning
RIGID uses a random forest forward model and MCMC sampling to generate metamaterial designs satisfying target functional responses, producing broader design-space coverage than genetic algorithms on acoustic and optical test cases with fewer than 250 training samples.