NSFnets

swMATH42059MaRDI QIDQ5972654

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• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations

Related Items (46)

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows ⋮ PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain ⋮ When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? ⋮ DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks ⋮ Using neural networks to accelerate the solution of the Boltzmann equation ⋮ PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations ⋮ Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation ⋮ Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection ⋮ Physics-informed neural networks for the shallow-water equations on the sphere ⋮ A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions ⋮ When and why PINNs fail to train: a neural tangent kernel perspective ⋮ A general neural particle method for hydrodynamics modeling ⋮ Meta-learning PINN loss functions ⋮ CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method ⋮ Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training ⋮ A decision-making machine learning approach in Hermite spectral approximations of partial differential equations ⋮ VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity ⋮ Scientific machine learning through physics-informed neural networks: where we are and what's next ⋮ Physics-informed PointNet: a deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries ⋮ Machine learning for vortex induced vibration in turbulent flow ⋮ ReF-nets: physics-informed neural network for Reynolds equation of gas bearing ⋮ Operator inference and physics-informed learning of low-dimensional models for incompressible flows ⋮ Improved deep neural networks with domain decomposition in solving partial differential equations ⋮ Galerkin neural network approximation of singularly-perturbed elliptic systems ⋮ Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons ⋮ Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations ⋮ Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios ⋮ Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions ⋮ Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) ⋮ Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning ⋮ An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator ⋮ An overview on deep learning-based approximation methods for partial differential equations ⋮ PDE-constrained models with neural network terms: optimization and global convergence ⋮ Physics informed neural networks: a case study for gas transport problems ⋮ On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks ⋮ Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations ⋮ Multi-Scale Deep Neural Network (MscaleDNN) Methods for Oscillatory Stokes Flows in Complex Domains ⋮ Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization ⋮ Computer simulation of water flow animation based on two-dimensional Navier-Stokes equations ⋮ PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs ⋮ Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains ⋮ Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems ⋮ Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response ⋮ Learning by neural networks under physical constraints for simulation in fluid mechanics ⋮ A physics-informed learning approach to Bernoulli-type free boundary problems ⋮ Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint

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