Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
type: Item
instance of: scholarly article
title: Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
author: Xu-Hui Zhou
author: Jiequn Han
author: Heng Xiao
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2021-12-13T00:00:00Z
full work available at URL: https://arxiv.org/abs/2103.06685
Mathematics Subject Classification ID: 76F65
Mathematics Subject Classification ID: 62P30
Mathematics Subject Classification ID: 65C20
Mathematics Subject Classification ID: 76F25
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 7442782
zbMATH Keywords: constitutive modeling
zbMATH Keywords: nonlocal closure model
zbMATH Keywords: symmetry and invariance
zbMATH Keywords: inverse modeling
zbMATH Keywords: deep learning
MaRDI profile type: Publication
OpenAlex ID: W3210404677
cites work: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
cites work: Data-driven computational mechanics
cites work: Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
cites work: Learning constitutive relations from indirect observations using deep neural networks
cites work: Learning constitutive relations using symmetric positive definite neural networks
cites work: Q5283943
cites work: A new hybrid turbulence modelling strategy for industrial CFD
cites work: Reassessment of the scale-determining equation for advanced turbulence models
cites work: Learning nonlocal constitutive models with neural networks
cites work: A more general effective-viscosity hypothesis
cites work: On explicit algebraic stress models for complex turbulent flows
cites work: Modelling the pressure–strain correlation of turbulence: an invariant dynamical systems approach
cites work: PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
cites work: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
cites work: Machine learning strategies for systems with invariance properties
cites work: Q4435456
cites work: Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling
cites work: Deep Potential: A General Representation of a Many-Body Potential Energy Surface
cites work: Turbulent Flows
cites work: Flow over periodic hills -- numerical and experimental study in a wide range of Reynolds numbers
cites work: Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations
cites work: Solution of the implicitly discretized fluid flow equations by operator- splitting
cites work: Q3263816
DOI: 10.1016/J.CMA.2021.114211
description: scientific article; zbMATH DE number 7442782