  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.
