DPM: a deep learning PDE augmentation method with application to large-eddy simulation
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Publication:2123852
DOI10.1016/j.jcp.2020.109811OpenAlexW3082908155MaRDI QIDQ2123852
Jonathan B. Freund, Jonathan F. MacArt, Justin A. Sirignano
Publication date: 14 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1911.09145
large-eddy simulationdeep learningturbulence simulationscientific machine learningsub-grid-scale modeling
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Uses Software
Cites Work
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- The use of explicit filters in large eddy simulation
- On the effect of numerical errors in large eddy simulations of turbulent flows
- Machine learning strategies for systems with invariance properties
- High order conservative finite difference scheme for variable density low Mach number turbulent flows
- Linear and nonlinear programming.
- Application of a fractional-step method to incompressible Navier-Stokes equations
- Effects of the computational time step on numerical solutions of turbulent flow
- Semi-implicit iterative methods for low Mach number turbulent reacting flows: operator splitting versus approximate factorization
- A further study of numerical errors in large-eddy simulations.
- An analysis of numerical errors in large-eddy simulations of turbulence
- DGM: a deep learning algorithm for solving partial differential equations
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Machine Learning for Fluid Mechanics
- Numerical Calculation of Time-Dependent Viscous Incompressible Flow of Fluid with Free Surface
- Numerical simulation of compressible homogeneous flows in the turbulent regime
- A dynamic subgrid-scale eddy viscosity model
- Subgrid-scale backscatter in turbulent and transitional flows
- Evaluation of subgrid-scale models using an accurately simulated turbulent flow
- Large-eddy simulation of the turbulent mixing layer
- Deep learning of vortex-induced vibrations
- DIRECT NUMERICAL SIMULATION: A Tool in Turbulence Research
- Optimal LES formulations for isotropic turbulence
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Discrete Conservation Properties of Unstructured Mesh Schemes
- Adjoint Equations in Stability Analysis
- Numerical Solution of the Navier-Stokes Equations
- S<scp>HAPE</scp> O<scp>PTIMIZATION IN</scp> F<scp>LUID</scp> M<scp>ECHANICS</scp>
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