Deep Reinforcement Learning for Radiative Heat Transfer Optimization Problems

TL;DR

This paper demonstrates how reinforcement learning can effectively optimize radiative heat transfer problems, outperforming traditional physical intuition-based solutions in complex multilayer metamaterials.

Contribution

It introduces a novel application of reinforcement learning to radiative heat transfer optimization, showcasing its potential to improve solutions over conventional methods.

Findings

Reinforcement learning algorithms outperform traditional approaches.

The method effectively optimizes near-field radiative heat transfer.

Potential for broad application in physical sciences.

Abstract

Reinforcement learning is a subfield of machine learning that is having a huge impact in the different conventional disciplines, including physical sciences. Here, we show how reinforcement learning methods can be applied to solve optimization problems in the context of radiative heat transfer. We illustrate their use with the optimization of the near-field radiative heat transfer between multilayer hyperbolic metamaterials. Specifically, we show how this problem can be formulated in the language of reinforcement learning and tackled with a variety of algorithms. We show that these algorithms allow us to find solutions that outperform those obtained using physical intuition. Overall, our work shows the power and potential of reinforcement learning methods for the investigation of a wide variety of problems in the context of radiative heat transfer and related topics.