Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
From MaRDI portal
Publication:2060111
DOI10.1016/j.cma.2021.114211OpenAlexW3210404677MaRDI QIDQ2060111
Xu-Hui Zhou, Jiequn Han, Heng Xiao
Publication date: 13 December 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.06685
Applications of statistics in engineering and industry; control charts (62P30) Probabilistic models, generic numerical methods in probability and statistics (65C20) Neural networks for/in biological studies, artificial life and related topics (92B20) Direct numerical and large eddy simulation of turbulence (76F65) Turbulent transport, mixing (76F25)
Related Items
Ensemble Kalman method for learning turbulence models from indirect observation data ⋮ Frame Invariance and Scalability of Neural Operators for Partial Differential Equations ⋮ An equivariant neural operator for developing nonlocal tensorial constitutive models
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Flow over periodic hills -- numerical and experimental study in a wide range of Reynolds numbers
- Machine learning strategies for systems with invariance properties
- Learning constitutive relations from indirect observations using deep neural networks
- Solution of the implicitly discretized fluid flow equations by operator- splitting
- Representation of stress tensor perturbations with application in machine-learning-assisted turbulence modeling
- Learning constitutive relations using symmetric positive definite neural networks
- Flows over periodic hills of parameterized geometries: a dataset for data-driven turbulence modeling from direct simulations
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
- Learning nonlocal constitutive models with neural networks
- Data-driven computational mechanics
- Modelling the pressure–strain correlation of turbulence: an invariant dynamical systems approach
- Reassessment of the scale-determining equation for advanced turbulence models
- A more general effective-viscosity hypothesis
- On explicit algebraic stress models for complex turbulent flows
- A new hybrid turbulence modelling strategy for industrial CFD
- Turbulent Flows
- Deep Potential: A General Representation of a Many-Body Potential Energy Surface
- Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
