articleTop 1% cited
Physics-informed neural networks for high-speed flows
Computer Methods in Applied Mechanics and Engineering · 2019 · Vol. 360 · pp. 112789–112789
Zhiping Mao(Brown University)Ameya D. Jagtap(Brown University)George Em Karniadakis✉(Brown University)
Model Reduction and Neural NetworksFluid Dynamics and Turbulent FlowsNuclear Engineering Thermal-HydraulicsEuler equationsConservation lawRiemann problemAerodynamicsInverse problemPosition (finance)Artificial neural networkMathematical analysisEuler's formulaApplied mathematics
Funding
- Air Force Office of Scientific Research
Citations
1,145
FWCI
42.35
field-weighted impact
References
57
Percentile
100%
vs. same field & year
Citations per year
Cited by
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
Computer Methods in Applied Mechanics and Engineering · 2020 · 1,012 citations
hp-VPINNs: Variational physics-informed neural networks with domain decomposition
Computer Methods in Applied Mechanics and Engineering · 2020 · 676 citations
References
Efficient Implementation of Weighted ENO Schemes
Journal of Computational Physics · 1996 · 6,354 citations
A survey of several finite difference methods for systems of nonlinear hyperbolic conservation laws
Journal of Computational Physics · 1978 · 2,568 citations
Hidden physics models: Machine learning of nonlinear partial differential equations
Journal of Computational Physics · 2017 · 1,315 citations
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Journal of Computational Physics · 2018 · 15,047 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.