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Top 23 Julia Sciml 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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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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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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NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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Optimization.jl
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
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OrdinaryDiffEq.jl
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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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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DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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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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NonlinearSolve.jl
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
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RecursiveArrayTools.jl
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
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NBodySimulator.jl
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
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ParameterizedFunctions.jl
A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications
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Julia Sciml discussion
Julia Sciml related posts
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Modelica
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Building a compile-time SIMD optimized smoothing filter
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Good linear algebra libraries
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Julia's latency: Past, present and future
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Please help me make a case to implement Julia in enterprise
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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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www.saashub.com | 8 Feb 2025
Index
What are some of the best open-source Sciml projects in Julia? This list will help you:
# | Project | Stars |
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1 | DifferentialEquations.jl | 2,899 |
2 | ModelingToolkit.jl | 1,464 |
3 | NeuralPDE.jl | 1,031 |
4 | Optimization.jl | 750 |
5 | SymbolicRegression.jl | 665 |
6 | OrdinaryDiffEq.jl | 574 |
7 | Catalyst.jl | 467 |
8 | DataDrivenDiffEq.jl | 409 |
9 | Surrogates.jl | 339 |
10 | SciMLSensitivity.jl | 333 |
11 | DiffEqBase.jl | 322 |
12 | DiffEqGPU.jl | 285 |
13 | StochasticDiffEq.jl | 266 |
14 | NonlinearSolve.jl | 245 |
15 | SciMLStyle | 217 |
16 | RecursiveArrayTools.jl | 217 |
17 | ReservoirComputing.jl | 210 |
18 | MethodOfLines.jl | 170 |
19 | LatticeQCD.jl | 148 |
20 | NBodySimulator.jl | 131 |
21 | ModelingToolkitStandardLibrary.jl | 130 |
22 | ParameterizedFunctions.jl | 76 |
23 | WorldDynamics.jl | 66 |