1 code implementation • 12 Sep 2023 • Andrea Beck, Marius Kurz
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.
3 code implementations • 21 Jun 2022 • Marius Kurz, Philipp Offenhäuser, Andrea Beck
We thus demonstrate that RL can provide a framework for consistent, accurate and stable turbulence modeling especially for implicitly filtered LES.
1 code implementation • 13 May 2022 • Marius Kurz, Philipp Offenhäuser, Dominic Viola, Oleksandr Shcherbakov, Michael Resch, Andrea Beck
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.
no code implementations • 25 Mar 2021 • Tizian Wenzel, Marius Kurz, Andrea Beck, Gabriele Santin, Bernard Haasdonk
Standard kernel methods for machine learning usually struggle when dealing with large datasets.
no code implementations • 23 Oct 2020 • Andrea Beck, Marius Kurz
This work presents a review of the current state of research in data-driven turbulence closure modeling.
no code implementations • 1 Oct 2020 • Marius Kurz, Andrea Beck
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.