A Parametric Physics-Informed Deep Learning Method for Probabilistic Design of Thermal Protection Systems
Precise and efficient calculations are necessary to accurately assess the effects of thermal protection system (TPS) uncertainties on aerospacecrafts. This paper presents a probabilistic design methodology for TPSs based on physics-informed neural networks (PINNs) with parametric uncertainty. A typi...
Main Authors: | Runlin Zhang, Nuo Xu, Kai Zhang, Lei Wang, Gui Lu |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-04-01
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Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/16/9/3820 |
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