SciML/DifferentialEquations.jl
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia. observed · 2026-08-28
Health v2 · maintenance only
95/100
- Activity 99
- Release rhythm 87
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 58.5
- age_days: 3766
- days_rel: 10
- days_push: 10
- n_releases_24m: 9
Adoption not part of the score
3151 stars · 256 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Julia suite of high-performance numerical solvers for differential equations, covering ODEs, SDEs, DDEs, DAEs, RODEs, jumps, and (S)PDEs, usable from Julia, Python, and R. It also provides scientific machine learning (SciML) components such as neural differential equations and includes some of the fastest implementations of classic and research-grade algorithms.
Use cases
- solve ordinary differential equations numerically
- simulate stochastic differential equations
- solve stiff ODE systems efficiently
- solve delay differential equations with history functions
- simulate differential-algebraic equations
- train neural differential equations for scientific machine learning
- simulate dynamical systems and Gillespie/Markov jump processes
- solve partial differential equations with finite difference or finite element methods
When to choose
- you need fast, high-accuracy solvers for ODEs, SDEs, DAEs, DDEs, or PDEs in Julia
- you want automatic algorithm selection and state-of-the-art performance beyond classic C/Fortran codes
- you need stiff large-scale systems with specialized linear solvers
- you want scientific machine learning features like neural differential equations
- you need high-precision or HPC-optimized integrators
When to avoid
- you need a simple one-off ODE solve in Python and prefer SciPy's familiar API
- you want symbolic/analytic solutions rather than numerical ones
- you cannot adopt the Julia ecosystem or its v8 breaking-change migration
- you only need a single equation type and prefer a lightweight specialized package
Facets
library · maturity active
simulation math machine-learning data-science mathematics data-science performance python cross-platform differential-equations ode sde dde dae pde scientific-computing sciml numerical-solvers neural-differential-equations algorithms julia
10 sources
- readme: https://github.com/SciML/DifferentialEquations.jl · fetched 2026-08-28 · a0c69c089451
- homepage: https://docs.sciml.ai/DiffEqDocs/stable/ · fetched 2026-08-29 · 90f13f0bbb77
- site_page: https://docs.sciml.ai/DiffEqDocs · fetched 2026-08-29 · 82260832e07c
- site_page: https://docs.sciml.ai/DiffEqDevDocs · fetched 2026-08-29 · db0de492634b
- site_page: https://docs.sciml.ai/DiffEqDocs/stable/getting_started · fetched 2026-08-29 · a11db7041cb3
- site_page: https://docs.sciml.ai/DiffEqDocs/stable/tutorials/faster_ode_example · fetched 2026-08-29 · 062ff9a300a4
- site_page: https://docs.sciml.ai/DiffEqDocs/stable/tutorials/advanced_ode_example · fetched 2026-08-29 · 9bd3fdc67fd7
- site_page: https://docs.sciml.ai/DiffEqDocs/stable/tutorials/sde_example · fetched 2026-08-29 · 033126865aae
- site_page: https://docs.sciml.ai/DiffEqDocs/stable/tutorials/rode_example · fetched 2026-08-29 · 940becb8b696
- site_page: https://docs.sciml.ai/DiffEqDocs/stable/tutorials/dde_example · fetched 2026-08-29 · 41e5407eeeb0
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| SciML/DifferentialEquations.jl | main | 95 |
For agents
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Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem