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

DOI10.1016/j.jcp.2020.109951OpenAlexW3010839048MaRDI QIDQ2127017

Xiaowei Jin, Shengze Cai, Hui Li, George Em. Karniadakis

Publication date: 19 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.06496

zbMATH Keywords

turbulenceill-posed problemsvelocity-pressure formulationtransfer learningvorticity-velocity formulationPINNs

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Numerical analysis (65-XX)

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Uses Software

• DiffSharp
• Adam
• DeepXDE

Cites Work

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• Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network
• A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence
• Machine Learning for Fluid Mechanics
• Exact fully 3D Navier–Stokes solutions for benchmarking
• Turbulent Flows
• Deep learning of vortex-induced vibrations
• Turbulence statistics in fully developed channel flow at low Reynolds number
• Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
• Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Turbulence Modeling in the Age of Data
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