Generative adversarial networks for data-scarce radiative heat transfer applications

Generative adversarial networks for data-scarce radiative heat transfer applications

Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation for data-scarce radiative heat transfer applications, a...

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Bibliographic Details
Main Authors: J J García-Esteban, J C Cuevas, J Bravo-Abad
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
generative adversarial networks
near-field radiative heat transfer
spectral applications
data scarcity
Online Access:https://doi.org/10.1088/2632-2153/ad33e1

Internet

https://doi.org/10.1088/2632-2153/ad33e1

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