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

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. • Thermal metamaterials empower us to manipulate thermal transport behavior. • Generative AI models are powerful tools to address the inverse design problems. • Conditional variational encoder efficiently addresses the inverse design problems. • The obtained metamaterial-based structure achieves thermal transparency. [ABSTRACT FROM AUTHOR]