Neural operator based Reynolds averaged turbulence modelling

DOI10.12941/JKSIAM.2024.28.108MaRDI QIDQ6647989

Seungtae Park, Hyungju Hwang, Junseung Ryu

Publication date: 3 December 2024

Published in: Journal of the Korean Society for Industrial and Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

Galilean invarianceneural operatorFourier neural operatorReynolds-averged Navier-Stokes

Mathematics Subject Classification ID

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

Cites Work

• Title not available (Why is that?)
• Machine learning strategies for systems with invariance properties
• Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations
• Data-driven modelling of the Reynolds stress tensor using random forests with invariance
• An evaluation of RANS turbulence modelling for aerodynamic applications
• A more general effective-viscosity hypothesis
• Large-eddy simulation of turbulent flow over a parametric set of bumps
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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