Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons

DOI10.1017/jfm.2022.738zbMath1498.76049arXiv2202.06988OpenAlexW4298000212MaRDI QIDQ5038552

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Publication date: 7 October 2022

Published in: Journal of Fluid Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2202.06988

zbMATH Keywords

incompressible Navier-Stokes equationsmachine learningconvolutional neural networktwo-dimensional homogeneous decaying turbulenceunder-resolved low-resolution solution

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Navier-Stokes equations for incompressible viscous fluids (76D05) Turbulence (76F99) Basic methods in fluid mechanics (76M99) Mathematical modeling or simulation for problems pertaining to fluid mechanics (76-10)

Related Items (4)

Comparison of neural closure models for discretised PDEs ⋮ A multifidelity deep operator network approach to closure for multiscale systems ⋮ Differentiable hybrid neural modeling for fluid-structure interaction ⋮ Towards high-accuracy deep learning inference of compressible flows over aerofoils

Uses Software

• TensorFlow
• Adam

Cites Work

• Unnamed Item
• Solution of the implicitly discretized fluid flow equations by operator- splitting
• Comparison of the SMAC, PISO and iterative time-advancing schemes for unsteady flows
• High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method
• Neural networks based subgrid scale modeling in large eddy simulations
• DPM: a deep learning PDE augmentation method with application to large-eddy simulation
• Prediction of aerodynamic flow fields using convolutional neural networks
• Recent advances on the numerical modelling of turbulent flows
• High-order methods for decaying two-dimensional homogeneous isotropic turbulence
• Turbulent Flows
• Subgrid modelling for two-dimensional turbulence using neural networks
• Direct simulation of a self-similar turbulent mixing layer
• A dynamic eddy-viscosity closure model for large eddy simulations of two-dimensional decaying turbulence
• Split energy–helicity cascades in three-dimensional homogeneous and isotropic turbulence
• Sensitivity of two-dimensional spatially developing mixing layers with respect to uncertain inflow conditions
• On the inviscid instability of the hyperbolictangent velocity profile
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Turbulence Modeling in the Age of Data
• Numerical simulation of developing and decaying two-dimensional turbulence
• Toward neural-network-based large eddy simulation: application to turbulent channel flow

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