Search Results for author:
Found 6 papers, 3 papers with code
Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning
The resulting optimized discretization yields more accurate results in LES than either the pure DG or FV method and renders itself as a viable modeling ansatz that could initiate a novel class of high-order schemes for compressible turbulence by combining turbulence modeling with shock capturing in a single framework.
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations
We thus demonstrate that RL can provide a framework for consistent, accurate and stable turbulence modeling especially for implicitly filtered LES.
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems
In order to leverage the potential of RL-enhanced CFD, the interaction between the CFD solver and the RL algorithm thus have to be implemented efficiently on high-performance computing (HPC) hardware.
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when dealing with large datasets.
A Perspective on Machine Learning Methods in Turbulence Modelling
This work presents a review of the current state of research in data-driven turbulence closure modeling.
A machine learning framework for LES closure terms
In the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data.
