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Top 9 Julia Sde Projects
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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.
Another up-and-coming solution is Julia's simulation ecosystem [1]. It is powered by the commercial organization behind the Julia programming language, which has received DARPA funding [2] to build out these tools. This ecosystem unifies researchers in numerical methods [3], scalable compute, and domain experts in modeling engineering systems (electrical, mechanical, etc.) I believe this is where simulation is headed.
[1] https://juliahub.com/products/juliasim
[2] https://news.ycombinator.com/item?id=26425659
[3] https://docs.sciml.ai/DiffEqDocs/stable/
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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
JuliaSim looks interesting! From my understanding, it's 100% proprietary/commercial, but built on top of the open source https://github.com/SciML/ModelingToolkit.jl?
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Catalyst.jl
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
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SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
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DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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DiffEqGPU.jl
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
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StochasticDiffEq.jl
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
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DiffEqDevTools.jl
Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Julia Sde discussion
Julia Sde related posts
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How much useful are Runge-Kutta methods of order 9 and higher within double-precision arithmetic/floating point accuracy?
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Interpolant Coefficients for the BS5 Runge-Kutta method
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Simulating a simple circuit with the ModelingToolkit
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‘Machine Scientists’ Distill the Laws of Physics from Raw Data
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Tutorials for Learning Runge-Kutta Methods with Julia?
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Julia 1.7 has been released
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Why are NonlinearSolve.jl and DiffEqOperators.jl incompatible with the latest versions of ModelingToolkit and Symbolics!!!? Symbolics and ModelingToolkit are heavily downgraded when those packages are added.
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www.saashub.com | 23 Jan 2025
Index
What are some of the best open-source Sde projects in Julia? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | DifferentialEquations.jl | 2,894 |
| 2 | ModelingToolkit.jl | 1,452 |
| 3 | Catalyst.jl | 468 |
| 4 | SciMLSensitivity.jl | 333 |
| 5 | DiffEqBase.jl | 320 |
| 6 | DiffEqGPU.jl | 284 |
| 7 | StochasticDiffEq.jl | 264 |
| 8 | ModelingToolkitStandardLibrary.jl | 127 |
| 9 | DiffEqDevTools.jl | 48 |
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