FV-PINN solves steady incompressible Navier-Stokes with a finite-volume, divergence-theorem loss that lowers the automatic-differentiation order from third to second derivatives of the stream function.
Tackling the curse of dimensionality with physics -informed neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
physics.flu-dyn 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Finite Volume Physical Informed Neural Network (FV-PINN) with Reduced Derivative Order for Incompressible Flows
FV-PINN solves steady incompressible Navier-Stokes with a finite-volume, divergence-theorem loss that lowers the automatic-differentiation order from third to second derivatives of the stream function.