SciML/ModelingToolkit.jl
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations 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: 0.0
- age_days: 3110
- days_rel: 7
- days_push: 7
- n_releases_24m: 277
Adoption not part of the score
1661 stars · 264 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
ModelingToolkit.jl is a Julia-based acausal, equation-based modeling framework and computer algebra system for symbolic-numeric computation in scientific machine learning. It lets users describe models at a high level, then automatically applies symbolic transformations (index reduction, simplification, sparsification, parallelization) and generates fast functions like Jacobians and Hessians for numerical solvers.
Use cases
- model and simulate systems of differential equations symbolically in Julia
- automatically generate fast Jacobians and Hessians for ODE solvers
- convert DAEs into optimization problems and vice versa
- build acausal physics-based component models like Modelica or Simulink
- parallelize and sparsify large scientific models automatically
- perform index reduction on differential-algebraic equations
- physics-informed machine learning with symbolic preprocessing
When to choose
- you need high-performance symbolic-numeric modeling in the Julia ecosystem
- you want acausal equation-based modeling with automatic transformations
- you need to bridge differential equations and optimization problems
- you want automatically parallelized, sparsity-aware model code generation
When to avoid
- you need a general-purpose CAS like SymPy or Mathematica outside scientific modeling
- your project is in Python, MATLAB, or another non-Julia language
- you only need to numerically solve standard ODEs without symbolic preprocessing
- you require a mature GUI-based modeling environment like Simulink
Facets
library · maturity active
simulation math compiler machine-learning data-science machine-learning mathematics simulation cross-platform acausal-modeling symbolic-computation differential-equations computer-algebra scientific-machine-learning ode dae pde equation-based-modeling julia scientific-computing algorithms
7 sources
- readme: https://github.com/SciML/ModelingToolkit.jl · fetched 2026-08-28 · 9c96637f0d12
- homepage: https://docs.sciml.ai/ModelingToolkit/stable/ · fetched 2026-08-29 · 0ec8554458d1
- 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/ModelingToolkit/stable/basics/FAQ · fetched 2026-08-29 · f81cc9903902
- site_page: https://docs.sciml.ai/Symbolics/stable · fetched 2026-08-29 · 5b363fc32024
- site_page: https://docs.sciml.ai/DiffEqDocs/stable · fetched 2026-08-29 · cd446df7f51e
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| SciML/ModelingToolkit.jl | main | 95 |
For agents
markdown · JSON · MCP: product_card(name="SciML/ModelingToolkit.jl")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem