Metamaterial-based realization for thermal transparency: A conditional variational autoencoder approach

Abstract

The advances in thermal metamaterials and their applications have revolutionized how we can manipulate thermal transport behavior. The challenging inverse design problems of utilizing thermal metamaterial-based structures to achieve desired thermal transport behavior are increasingly being tackled by data-driven, machine learning-based approaches. The explosive progress in generative AI is permeating the field of material design by offering new perspectives to address the inverse design problems. In this paper, we propose a simple yet effective method of training a generative conditional variational autoencoder to find the design parameters for a thermal metamaterial-based system with a periodic interparticle arrangement to achieve thermal transparency, which is one of the most desirable and interesting thermal transport behaviors. Our work attests to the predictive power of a generative model with a relatively small number of parameters for the purpose of tackling inverse design problems to achieve thermal transport behavior manipulation.