Deep learning for the modeling and inverse design of radiative heat transfer
Entity
UAM. Departamento de Física Teórica de la Materia CondensadaPublisher
American Physical SocietyDate
2021-12-02Citation
10.1103/PhysRevApplied.16.064006
Physical Review Applied 16.6 (2021): 064006
ISSN
2331-7019 (online)DOI
10.1103/PhysRevApplied.16.064006Project
Gobierno de España. RTI2018-098452-B-I00; Gobierno de España. PID2020-114880GB-I00Editor's Version
https://doi.org/10.1103/PhysRevApplied.16.064006Subjects
Cooling Systems; Deep Neural Networks; Inverse Problems; Network Architecture; Numerical Methods; FísicaRights
© 2021 American Physical SocietyAbstract
Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines,
including the 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 data sets, 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 data sets 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
Files in this item
Google Scholar:García Esteban, Juan José
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Bravo Abad, Jorge
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Cuevas Rodríguez, Juan Carlos
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