Reinforcement learning approach to thermal transparency with particles in periodic lattices

Abstract

Implementing thermal transparency by using thermal metamaterials, with its potential applications in real-world scenarios, has been a promising field attracting many theoretical and experimental studies. The implementation of thermal transparency, as well as other thermal metamaterial-based applications, often requires solving an inverse design problem to calculate optimal design parameters. In this paper, we propose a periodic interparticle interaction mechanism to realize thermal transparency, in which particles are arranged in periodic lattices with symmetric interactions and anisotropic thermal conductivities. We reframe the inverse design problem of calculating the design parameters of such a periodic interparticle system into a reinforcement learning problem. The essence of our reinforcement learning-based approach is to train an intelligent agent that can vary the design parameters in a series of time steps toward the realization of thermal transparency. Compared to our previous effort to solve the same problem with an autoencoder-based approach, the reinforcement learning-based approach requires significantly less computational resources and thus demonstrates its potential to alleviate the “curse of dimensionality.” We also discuss the cause for the superior computational efficiency of the reinforcement learning-based approach over the autoencoder-based approach, and the possibility of extending the use of our reinforcement learning-based approach to solve other inverse design problems.