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.
Interpretable forward and inverse design of particle spectral emissivity using common machine-learning models
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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.