New Preprint ‘Adaptive numerical simulations with Trixi.jl: A case study of Julia for scientific computing’ on arXiv

Michael Schlottke-Lakemper, Andrew R. Winters, Erik Faulhaber, Jesse Chan, Gregor J. Gassner, and I have published our new preprint Adaptive numerical simulations with Trixi.jl: A case study of Julia for scientific computing on arXiv.

We present Trixi.jl, a Julia package for adaptive high-order numerical simulations of hyperbolic partial differential equations. Utilizing Julia’s strengths, Trixi.jl is extensible, easy to use, and fast. We describe the main design choices that enable these features and compare Trixi.jl with a mature open source Fortran code that uses the same numerical methods. We conclude with an assessment of Julia for simulation-focused scientific computing, an area that is still dominated by traditional high-performance computing languages such as C, C++, and Fortran.