Publication | Open Access
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
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Citations
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References
2020
Year
Compressible FlowEngineeringIncompressible FlowIncompressible Navier-stokes EquationsPhysic Aware Machine LearningFluid MechanicsNumerical SimulationNavier-stokes EquationsDeep LearningNavier-stokes Flow NetsHydrodynamic StabilityPhysics-informed Neural Networks
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Maziar Raissi, Paris Perdikaris, George Em Karniadakis Journal of Computational Physics EngineeringPde-constrained OptimizationDeep Learning FrameworkAi FoundationInverse Problems | 2018 | 14.4K |
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