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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Trixi.jl: Adaptive high-order numerical simulations of conservation laws in Julia
| Date | Stars |
|---|---|
| 2026-07-25 | 712 |
| 2026-07-28 | 712 |
| 2026-07-30 | 712 |
| 2026-08-06 | 712 |
Today
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Momentum
35.0
growth rate 0.00%/day
# Trixi.jl [](https://trixi-framework.github.io/TrixiDocumentation/stable) [](https://trixi-framework.github.io/TrixiDocumentation/dev) [](https://join.slack.com/t/trixi-framework/shared_invite/zt-sgkc6ppw-6OXJqZAD5SPjBYqLd8MU~g) [](https://www.youtube.com/@trixi-framework) [](https://github.com/trixi-framework/Trixi.jl/actions?query=workflow%3ACI) [](https://codecov.io/gh/trixi-framework/Trixi.jl) [](https://coveralls.io/github/trixi-framework/Trixi.jl?branch=main) [](https://github.com/JuliaTesting/Aqua.jl) [](https://opensource.org/licenses/MIT) [](https://doi.org/10.5281/zenodo.3996439) [](https://www.bestpractices.dev/projects/8695) <!-- [](https://pkgs.genieframework.com?packages=Trixi) --> <!-- [](https://github.com/trixi-framework/Trixi.jl) --> <!-- [](https://juliaci.github.io/NanosoldierReports/pkgeval_badges/report.html) --> <p align="center"> <img width="300px" src="https://trixi-framework.github.io/assets/logo.png"> </p> **Trixi.jl** is a numerical simulation framework for conservation laws written in [Julia](https://julialang.org). A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures. Its features include: * 1D, 2D, and 3D simulations on [line/quad/hex/simplex meshes](https://trixi-framework.github.io/TrixiDocumentation/stable/overview/#Semidiscretizations) * Cartesian and curvilinear meshes * Conforming and non-conforming meshes * Structured and unstructured meshes * Hierarchical quadtree/octree grid with adaptive mesh refinement * Forests of quadtrees/octrees with [p4est](https://github.com/cburstedde/p4est) via [P4est.jl](https://github.com/trixi-framework/P4est.jl) * High-order accuracy in space and time * Arbitrary floating-point precision * Discontinuous Galerkin methods * Kinetic energy-preserving and entropy-stable methods based on flux differencing * Entropy-stable shock capturing * [Finite difference summation by parts (SBP) methods](https://github.com/ranocha/SummationByPartsOperators.jl) * Advanced limiting strategies * Positivity-preserving limiting * Subcell invariant domain-preserving (IDP) limiting * Entropy-bounded limiting * Compatible with the [SciML ecosystem for ordinary differential equations](https://diffeq.sciml.ai/latest/) * [Explicit low-storage Runge-Kutta time integration](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Low-Storage-Methods) * [Strong stability preserving methods](https://diffeq.sciml.ai/latest/solvers/ode_solve/#Explicit-Strong-Stability-Preserving-Runge-Kutta-Methods-for-Hyperbolic-PDEs-(Conservation-Laws)) * CFL-based and error-based time step control * Cust
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:59cfa0f97a563f62, topic:simulation
