Least-square finite difference-based physics-informed neural network for steady incompressible flows

Cites Work

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Numerical solutions of 2-D steady incompressible flow over a backward-facing step. I: High Reynolds number solutions
Numerical comparison of least square-based finite-difference (LSFD) and radial basis function-based finite-difference (RBFFD) methods
Free vibration analysis of plates using least-square-based finite difference method
The dispersion of the Hammersley sequence in the unit square
Development of least-square-based two-dimensional finite difference schemes and their application to simulate natural convection in a cavity.
Randomized Halton sequences
Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method
Physics-informed neural networks for inverse problems in supersonic flows
Variational physics informed neural networks: the role of quadratures and test functions
Physics-informed neural networks for high-speed flows
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
An adaptive finite volume method for the incompressible Navier-Stokes equations in complex geometries
Finite difference method for incompressible Navier--Stokes equations in arbitrary orthogonal curvilinear coordinates
A local and parallel Uzawa finite element method for the generalized Navier-Stokes equations
Navier-Stokes equations. Theory and numerical analysis. Repr. with corr
Lattice Boltzmann and Gas Kinetic Flux Solvers
Comparison of method of lines and finite difference solutions of 2-D Navier-Stokes equations for transient laminar pipe flow
A numerical study of flow over a confined backward‐facing step
Study of the mixed finite volume method for Stokes and Navier‐Stokes equations
Finite element interpolated neural networks for solving forward and inverse problems
Learning of viscosity functions in rarefied gas flows with physics-informed neural networks
Discontinuity computing using physics-informed neural networks
Applications of finite difference-based physics-informed neural networks to steady incompressible isothermal and thermal flows

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