A machine learning framework for LES closure terms

DOI10.1553/etna_vol56s117zbMath1490.76118arXiv2010.03030OpenAlexW4206156131MaRDI QIDQ2672196

Marius Kurz, Andrea Beck

Publication date: 8 June 2022

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.03030

zbMATH Keywords

large eddy simulationartificial neural networksrecurrent neural networksturbulence modelsdeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Direct numerical and large eddy simulation of turbulence (76F65) Isotropic turbulence; homogeneous turbulence (76F05)

Related Items (1)

Data-driven fractional subgrid-scale modeling for scalar turbulence: a nonlocal LES approach

Uses Software

• TensorFlow
• PyTorch
• ImageNet
• Adam
• AlphaZero
• FLEXI
• AlexNet

Cites Work

• Neural networks based subgrid scale modeling in large eddy simulations
• FLEXI: a high order discontinuous Galerkin framework for hyperbolic-parabolic conservation laws
• Self-conditioned fields for large-eddy simulations of turbulent flows
• Large Eddy Simulation for Compressible Flows
• The decay of axisymmetric homogeneous turbulence
• Subgrid modelling for two-dimensional turbulence using neural networks
• A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
• A neural network approach for the blind deconvolution of turbulent flows
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
• Statistical Properties of Subgrid-Scale Turbulence Models
• Homogeneous Turbulence Dynamics
• Approximation by superpositions of a sigmoidal function

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