RoeNet: Predicting discontinuity of hyperbolic systems from continuous data

Predicting future discontinuous phenomena that are unobservable from training data sets has long been a challenging problem in scientific machine learning. We introduce a novel paradigm to predict the emergence and evolution of various discontinuities of hyperbolic partial differential equations (PD...

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Veröffentlicht in:	International journal for numerical methods in engineering 2024-03, Vol.125 (6), p.n/a
Hauptverfasser:	Tong, Yunjin, Xiong, Shiying, He, Xingzhe, Yang, Shuqi, Wang, Zhecheng, Tao, Rui, Liu, Runze, Zhu, Bo
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks conservation law Discontinuity Hyperbolic differential equations Hyperbolic systems Machine learning Partial differential equations physics‐informed neural network Robustness (mathematics) Roe solver Solvers
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