Trixi Framework

The Trixi framework is a collaborative scientific effort to provide open source tools for adaptive high-order numerical simulations of hyperbolic PDEs in Julia. Besides the core algorithms, the framework also includes mesh and visualization tools. Moreover, it includes utilities such as Julia wrappers of mature libraries written in other programming languages.

This page gives an overview of the different activities that, together, constitute the Trixi framework on GitHub.

  1. Adaptive high-order numerical simulations of hyperbolic PDEs
  2. Mesh generation
  3. Particle-based multiphysics simulations
  4. Additional packages
  5. Publications
  6. Talks
  7. Outreach
  8. Authors
  9. Get in touch!
  10. Acknowledgments

Adaptive high-order numerical simulations of hyperbolic PDEs

Mesh generation

Particle-based multiphysics simulations

Additional packages

Publications

The following publications make use of Trixi.jl or one of the other packages listed above. Author names of Trixi.jl's main developers are in italics.

2024

2023

2022

2021

Talks

2024

2023

2022

2021

Outreach

Google Summer of Code 2023

Trixi.jl participated in the Google Summer of Code 2023, marking its initial steps towards running on GPUs. This project was mentored by Hendrik Ranocha and Michael Schlottke-Lakemper. Here you can find the report from our contributor Huiyu Xie.

Authors

Michael Schlottke-Lakemper (University of Augsburg, Germany), Gregor Gassner (University of Cologne, Germany), Hendrik Ranocha (University of Hamburg, Germany), Andrew Winters (Linköping University, Sweden), and Jesse Chan (Rice University, US) are the principal developers of Trixi.jl. David A. Kopriva (Florida State University, US) is the principal developer of HOHQMesh and HOHQMesh.jl. For a full list of authors, please check out the respective packages.

Get in touch!

There are a number of ways to reach out to us:

Acknowledgments

This project has benefited from funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the following grants:

This project has benefited from funding from the European Research Council through the ERC Starting Grant "An Exascale aware and Un-crashable Space-Time-Adaptive Discontinuous Spectral Element Solver for Non-Linear Conservation Laws" (Extreme), ERC grant agreement no. 714487.

This project has benefited from funding from Vetenskapsrådet (VR, Swedish Research Council), Sweden through the VR Starting Grant "Shallow water flows including sediment transport and morphodynamics", VR grant agreement 2020-03642 VR.

This project has benefited from funding from the United States National Science Foundation (NSF) under awards DMS-1719818 and DMS-1943186.

This project has benefited from funding from the German Federal Ministry of Education and Research (BMBF) through the project grant "Adaptive earth system modeling with significantly reduced computation time for exascale supercomputers (ADAPTEX)" (funding id: 16ME0668K).

This project has benefited from funding by the Daimler und Benz Stiftung (Daimler and Benz Foundation) through grant no. 32-10/22.

Trixi.jl is supported by NumFOCUS as an Affiliated Project.