The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation
From MaRDI portal
Publication:6185144
DOI10.1016/j.cma.2023.116543OpenAlexW4388792441MaRDI QIDQ6185144
No author found.
Publication date: 29 January 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2023.116543
Cites Work
- Unnamed Item
- Unnamed Item
- Neural network modeling for near wall turbulent flow.
- A painless intrusive polynomial chaos method with RANS-based applications
- Circumventing the solution of inverse problems in mechanics through deep learning: application to elasticity imaging
- Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures
- GINNs: graph-informed neural networks for multiscale physics
- Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
- Learning viscoelasticity models from indirect data using deep neural networks
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics
- Analysis of the two-dimensional dynamics of a Mach 1.6 shock wave/transitional boundary layer interaction using a RANS based resolvent approach
- Multiscale and Multiresolution Approaches in Turbulence
- Tempered fractional LES modeling
- Kinetic-energy-flux-constrained model using an artificial neural network for large-eddy simulation of compressible wall-bounded turbulence
- Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling
- Unsupervised deep learning for super-resolution reconstruction of turbulence
- A seamless multiscale operator neural network for inferring bubble dynamics
- Flow physics and RANS modelling of oblique shock/turbulent boundary layer interaction
- A neural network approach for the blind deconvolution of turbulent flows
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Layering, Instabilities, and Mixing in Turbulent Stratified Flows
- Statistical Properties of Subgrid-Scale Turbulence Models