article Open AccessTop 1% cited
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Journal of Computational Physics · 2018 · Vol. 378 · pp. 686–707
Maziar Raissi(Brown University)Paris Perdikaris✉(University of Pennsylvania)George Em Karniadakis(Brown University)
Model Reduction and Neural NetworksFluid Dynamics and Turbulent FlowsMeteorological Phenomena and SimulationsPartial differential equationNonlinear systemArtificial neural networkContext (archaeology)Inverse problemPartial derivativeComputer scienceAutomatic differentiationApplied mathematicsPhysical law
Funding
- U.S. Department of Energy
- Defense Advanced Research Projects Agency
- Air Force Office of Scientific Research
Citations
15,047
FWCI
267.73
field-weighted impact
References
64
Percentile
100%
vs. same field & year
Citations per year
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References
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AIChE Journal · 1992 · 931 citations
Multilayer feedforward networks are universal approximators
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IEEE Transactions on Neural Networks · 1998 · 2,128 citations
ImageNet classification with deep convolutional neural networks
Communications of the ACM · 2017 · 75,550 citations
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Proceedings of the National Academy of Sciences · 2016 · 4,342 citations
Data-driven discovery of partial differential equations
Science Advances · 2017 · 1,500 citations
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