Deep learning for the modeling and inverse design of radiative heat transfer

TL;DR

This paper demonstrates that deep neural networks can effectively model and optimize various radiative heat transfer phenomena, providing fast, accurate surrogates and inverse design tools across different physical systems.

Contribution

The study introduces a neural network-based approach for modeling and inverse design in radiative heat transfer, applicable to diverse physical problems with moderate training datasets.

Findings

Neural networks accurately model near-field heat transfer in hyperbolic metamaterials.

Deep learning enables fast inverse design of radiative cooling structures.

The approach is versatile across different radiative heat transfer problems.

Abstract

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting an increasing attention in many other disciplines, including physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative heat transfer phenomena and devices. By using a set of custom-designed numerical methods able to efficiently generate the required training datasets, we demonstrate this approach in the context of three very different problems, namely, (i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials, (ii) passive radiate cooling in photonic-crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple neural network architectures trained with datasets of moderate size can be used as fast and accurate surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiation.