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...

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Bibliographic Details
Main Authors:	Lu, G. (Author), Wang, L. (Author), Xu, N. (Author), Zhang, K. (Author), Zhang, R. (Author)
Format:	Article
Language:	English
Published:	MDPI 2023
Subjects:	Computational efficiency Deep learning Design Heat shielding Insulation materials Learning methods Learning systems Neural network model Neural network models Neural-networks Physic-informed neural network physics-informed neural networks Probabilistic design Sensitivity analysis surrogate model Surrogate modeling System uncertainties Thermal insulating materials Thermal insulation thermal protection system Thermal Protection System Uncertainty analysis uncertainty quantification Uncertainty quantifications
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A Parametric Physics-Informed Deep Learning Method for Probabilistic Design of Thermal Protection Systems

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