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.
Past achievements and future challenges in the development of three-dimensional photonic metamaterials
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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.