143 citations · 348 across the 12 of their papers we have counts for
12 papers
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…
Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks
Marius Kurz, Andrea Beck, Benjamin Sanderse
This work proposes a novel methodology for turbulence modeling in Large Eddy Simulation (LES) based on Graph Neural Networks (GNNs), which embeds the discrete rotational, reflectio…
Invariant Control Strategies for Active Flow Control using Graph Neural Networks
Marius Kurz, Rohan Kaushik, Marcel Blind +4
Reinforcement learning has gained traction for active flow control tasks, with initial applications exploring drag mitigation via flow field augmentation around a two-dimensional c…
GALÆXI: Solving complex compressible flows with high-order discontinuous Galerkin methods on accelerator-based systems
Daniel Kempf, Marius Kurz, Marcel Blind +6
This work presents GALAEXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured meshes leveraging the parallel computing power of moder…
Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning
Andrea Beck, Marius Kurz
This study proposes a novel method for developing discretization-consistent closure schemes for implicitly filtered Large Eddy Simulation (LES). Here, the induced filter kernel, an…
Towards Exascale CFD Simulations Using the Discontinuous Galerkin Solver FLEXI
Marcel Blind, Min Gao, Daniel Kempf +4
Modern high-order discretizations bear considerable potential for the exascale era due to their high fidelity and the high, local computational load that allows for computational e…
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations
Marius Kurz, Philipp Offenhäuser, Andrea Beck
Over the last years, supervised learning (SL) has established itself as the state-of-the-art for data-driven turbulence modeling. In the SL paradigm, models are trained based on a…
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems
Marius Kurz, Philipp Offenhäuser, Dominic Viola +3
Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dynamical systems. A prominent instance of such a dynamical system is the system of…
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Tizian Wenzel, Marius Kurz, Andrea Beck +2
Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that…
A Perspective on Machine Learning Methods in Turbulence Modelling
Andrea Beck, Marius Kurz
This work presents a review of the current state of research in data-driven turbulence closure modeling. It offers a perspective on the challenges and open issues, but also on the…
A machine learning framework for LES closure terms
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. To t…
An Efficient Sliding Mesh Interface Method for High-Order Discontinuous Galerkin Schemes
Jakob Dürrwächter, Marius Kurz, Patrick Kopper +3
Sliding meshes are a powerful method to treat deformed domains in computational fluid dynamics, where different parts of the domain are in relative motion. In this paper, we presen…