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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Veröffentlicht in:	Machine learning: science and technology 2024-03, Vol.5 (1), p.15060
Hauptverfasser:	García-Esteban, J J, Cuevas, J C, Bravo-Abad, J
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence data scarcity Generative adversarial networks Generative artificial intelligence Heat transfer Image processing Machine learning Metamaterials near-field radiative heat transfer Neural networks Radiative heat transfer spectral applications
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