Deep-learning of parametric partial differential equations from sparse and noisy data

Data-driven methods have recently made great progress in the discovery of partial differential equations (PDEs) from spatial-temporal data. However, several challenges remain to be solved, including sparse noisy data, incomplete library, and spatially or temporally varying coefficients. In this work...

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Veröffentlicht in:	Physics of fluids (1994) 2021-03, Vol.33 (3)
Hauptverfasser:	Xu, Hao, Zhang, Dongxiao, Zeng, Junsheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Burgers equation Coefficients Convection-diffusion equation Deep learning Fluid dynamics Genetic algorithms Libraries Mathematical analysis Neural networks Partial differential equations Physics Spatiotemporal data Wave equations
Online-Zugang:	Volltext
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