article Open AccessTop 1% cited
hp-VPINNs: Variational physics-informed neural networks with domain decomposition
Computer Methods in Applied Mechanics and Engineering · 2020 · Vol. 374 · pp. 113547–113547
Ehsan Kharazmi✉(Brown University)Zhongqiang Zhang(Worcester Polytechnic Institute)George Em Karniadakis(Pacific Northwest National Laboratory)
Model Reduction and Neural NetworksMagnetic Properties and ApplicationsFluid Dynamics and Turbulent FlowsArtificial neural networkFunction approximationDomain (mathematical analysis)PiecewiseProjection (relational algebra)Domain decomposition methodsNonlinear systemMathematicsSpace (punctuation)Applied mathematics
Funding
- U.S. Department of Defense
- U.S. Department of Energy
- Defense Advanced Research Projects Agency
Citations
676
FWCI
32.20
field-weighted impact
References
68
Percentile
100%
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References
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Neurocomputing · 2018 · 631 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
Physics-informed neural networks for high-speed flows
Computer Methods in Applied Mechanics and Engineering · 2019 · 1,145 citations
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
Computer Methods in Applied Mechanics and Engineering · 2020 · 1,823 citations
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