FLEXI: A high order discontinuous Galerkin framework for hyperbolic-parabolic conservation laws
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Abstract (arXiv)
High order (HO) schemes are attractive candidates for the numerical solution of multiscale problems occurring in fluid dynamics and related disciplines. Among the HO discretization variants, discontinuous Galerkin schemes offer a collection of advantageous features which have lead to a strong increase in interest in them and related formulations in the last decade. The methods have matured sufficiently to be of practical use for a range of problems, for example in direct numerical and large eddy simulation of turbulence. However, in order to take full advantage of the potential benefits of these methods, all steps in the simulation chain must be designed and executed with HO in mind. Especially in this area, many commercially available closed-source solutions fall short. In this work, we therefor present the FLEXI framework, a HO consistent, open-source simulation tool chain for solving the compressible Navier-Stokes equations in a high performance computing setting. We describe the numerical algorithms and implementation details and give an overview of the features and capabilities of all parts of the framework. Beyond these technical details, we also discuss the important, but often overlooked issues of code stability, reproducibility and user-friendliness. The benefits gained by developing an open-source framework are discussed, with a particular focus on usability for the open-source community. We close with sample applications that demonstrate the wide range of use cases and the expandability of FLEXI and an overview of current and future developments.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Oct 2019.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2022Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulationscited by 16
- 2022Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systemscited by 7
- 2023Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learningcited by 5
- 2025Harnessing Equivariance: Modeling Turbulence with Graph Neural Networkscited by 5
- 2023Gym-preCICE: Reinforcement Learning Environments for Active Flow Controlcited by 2
- 2026Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Controlcited by 2
- 2025Invariant Control Strategies for Active Flow Control using Graph Neural Networkscited by 1
- 2026Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control21 Jan 2026
- 2025Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks10 Apr 2025
- 2025Invariant Control Strategies for Active Flow Control using Graph Neural Networks28 Mar 2025
- 2023Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning12 Sep 2023
- 2023Gym-preCICE: Reinforcement Learning Environments for Active Flow Control3 May 2023
- 2022Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations21 Jun 2022
- 2022Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems13 May 2022