Performance prediction of VO2-based smart radiation devices through semi-self-supervised learning with phase transition adaptation

Performance prediction of VO2-based smart radiation devices through semi-self-supervised learning with phase transition adaptation

Accurately forecasting the infrared radiation properties of multilayer systems exhibiting phase transition behavior presents a formidable challenge. In this study, we propose a physically-inspired Phase Transition Adaptation Model (PTAM) that leverages a deep neural network with a branching architec...

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Bibliographic Details
Main Authors: Yanyu Chen, Tao Zhao, Yanke Chang, Jinxin Gu, Wei Ma, Shuliang Dou, Yao Li
Format: Article
Language:English
Published: Elsevier 2024-04-01
Series:Next Energy
Subjects:
Neural network
Deep learning
Radiation regulation
Vanadium dioxide
Online Access:http://www.sciencedirect.com/science/article/pii/S2949821X23000455

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http://www.sciencedirect.com/science/article/pii/S2949821X23000455

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