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Physics-Informed Neural Networks for Heat Transfer Problems

Journal of heat transfer, 2021
Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics.
Shengze Cai   +4 more
semanticscholar   +1 more source

Status, Challenges, and Potential for Machine Learning in Understanding and Applying Heat Transfer Phenomena

Journal of heat transfer, 2021
Machine learning (ML) offers a variety of techniques to understand many complex problems in different fields. The field of heat transfer, and thermal systems in general, are governed by complicated sets of governing physics that can be made tractable ...
Matthew L. Hughes, G. Kini, S. Garimella
semanticscholar   +1 more source

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