Bayesian Nonparametric Modeling for Rapid Design of Metamaterial Microstructures

Abstract EN

We consider the problem of rapid design of massive metamaterial (MTM) microstructures from a statistical point of view.

A Bayesian nonparametric model, namely, Gaussian Process (GP) mixture, is developed to generate the mapping relationship from the microstructure’s geometric dimension to the electromagnetic response, which is approximately expressed in a closed form of Drude-Lorentz type model.

This GP mixture model is neatly able to tackle nonstationarity, discontinuities in the mapping function.

The inference is performed using a Markov chain relying on Gibbs sampling.

Experimental results demonstrate that the proposed approach is highly efficient in facilitating rapid design of MTM with accuracy.