Reducing data-driven dynamical subgrid scale models by physical constraints

DOI10.1016/j.compfluid.2020.104470OpenAlexW3005348600MaRDI QIDQ2176859

Daan Crommelin, Wouter Edeling

Publication date: 5 May 2020

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://ir.cwi.nl/pub/29479

zbMATH Keywords

turbulencesubgrid scale modelsurrogate modelsdata-driven

Mathematics Subject Classification ID

Fluid mechanics (76-XX)

Related Items (1)

Resampling with neural networks for stochastic parameterization in multiscale systems

Uses Software

• GitHub
• vorticity-solver

Cites Work

• Computing nearly singular solutions using pseudo-spectral methods
• Covariate-based stochastic parameterization of baroclinic ocean eddies
• Direct control of the small-scale energy balance in two-dimensional fluid dynamics
• Subgrid modelling for two-dimensional turbulence using neural networks
• Random-forcing model of the mesoscale oceanic eddies
• The emergence of isolated coherent vortices in turbulent flow
• Spectral Methods

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