Kinetic.jl
Kinetic.jl: Free, self-hosted Julia software for kinetic-flow research on CPUs or GPUs. Ranked #22 of 89 in Physics Simulation Software by our editors (6.9/10); pricing: Free plan; best for researchers simulating kinetic flows on CPUs or GPUs.
At a glance
- Editor score6.9 / 10
- PricingFree plan
- Best forResearchers simulating kinetic flows on CPUs or GPUs
- Free planYes
- Paid fromFree
- Delivery modeDesktop
- Facts checked24 Sep 2026

Where it wins
- Handles Boltzmann, Euler, Navier–Stokes and other documented equations
- Runs one- to three-dimensional simulations on CPUs and GPUs
- Adds parallel execution, machine-learning methods and a Python interface
Where it doesn't
- Requires a self-hosted Julia workflow and local technical setup
- Focused on kinetic and fluid equations rather than general simulation domains
- Support is centered on community channels and documentation
Our verdict on Kinetic.jl
Kinetic.jl is an open-source computational fluid dynamics toolbox written in Julia. It models particle transport and fluid-dynamics problems across kinetic theory and continuum mechanics, with support for Boltzmann, Euler, Navier–Stokes, Maxwell and other documented equations. One- to three-dimensional numerical simulations can run on CPUs or GPUs. The project is aimed at scientific research and educational work in kinetic theory, making it a fit for researchers and technically oriented teams who need direct control over simulation code.
The software is free and self-hosted, so there is no paid tier to compare. Its ecosystem is centered on Julia, with CUDA support for GPU execution, DifferentialEquations.jl and Flux integrations, and kineticpy for a Python interface. KitBase supplies physical models and numerical schemes, while KitML adds neural models and machine-learning methods. Parallel options include threaded and distributed execution. This combination suits workflows that already use Julia or need to connect Julia simulations with Python-based analysis.
Kinetic.jl’s standout strength is the breadth within its kinetic-flow focus: users can combine established equations, numerical schemes, CPU or GPU execution, and machine-learning approaches in one toolbox. The trade-off is a self-managed workflow that assumes comfort with Julia, local installation and computational modeling. Community support and documentation are the stated support channels rather than a managed service. Choose Kinetic.jl when particle transport, fluid equations, parallel computing or research experimentation are central requirements. Choose a broader general-purpose simulator when your work extends beyond these physics domains or calls for a more guided application experience.
Your product? Our editors can test Kinetic.jl in depth and publish a review, labelled as paid · from $149/mo →
Kinetic.jl pricing
Kinetic.jl fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Delivery mode | Desktop |
| Lesson library | No |
| Authoring tools | No |
| Assessment tools | No |
| 3D visuals | No |
| Offline access | Yes |
| Data export | Not verified |
| Deployment | Self-hosted |
| Platforms | Linux, Windows, macOS |
| Support | Community, Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 5 integrations: Julia, CUDA, Python (kineticpy), DifferentialEquations.jl, Flux |
| Pricing | Free plan |
| Website | xiaotianbai.com |
| Facts checked | 24 Sep 2026 |
Kinetic.jl integrations
Kinetic.jl lists 5 integrations on its own site.
- Julia
- CUDA
- Python (kineticpy)
- DifferentialEquations.jl
- Flux
Alternatives to Kinetic.jl
- COMSOL MultiphysicsA broad desktop environment for coupled physics, engineering models, and custom simulation apps.9.4
- Ansys FluentA deep CFD and HPC environment for demanding industrial simulation work.9.3
- SimScaleA broad browser-based multiphysics platform with flexible cloud workflows and tiered access.9.1
See all Kinetic.jl alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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