Top 23 Julia scientific-machine-learning Projects

• DifferentialEquations.jl

  6 2,756 7.2 Julia

  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.

  

• ModelingToolkit.jl

  15 1,335 9.8 Julia

  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

  

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• NeuralPDE.jl

  10 903 9.7 Julia

  Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

  

Project mention: Automatically install huge number of dependency? | /r/Julia | 2023-05-31

The documentation has a manifest associated with it: https://docs.sciml.ai/NeuralPDE/dev/#Reproducibility. Instantiating the manifest will give you all of the exact versions used for the documentation build (https://github.com/SciML/NeuralPDE.jl/blob/gh-pages/v5.7.0/assets/Manifest.toml). You just ]instantiate folder_of_manifest. Or you can use the Project.toml.

• DiffEqFlux.jl

  2 835 8.9 Julia

  Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

  

• Optimization.jl

  3 663 9.7 Julia

  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.

  

Project mention: SciPy: Interested in adopting PRIMA, but little appetite for more Fortran code | news.ycombinator.com | 2023-05-18

Interesting response. I develop the Julia SciML organization https://sciml.ai/ and we'd be more than happy to work with you to get wrappers for PRIMA into Optimization.jl's general interface (https://docs.sciml.ai/Optimization/stable/). Please get in touch and we can figure out how to set this all up. I personally would be curious to try this out and do some benchmarks against nlopt methods.

• OrdinaryDiffEq.jl

  3 500 9.6 Julia

  High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

  

Project mention: Modern Numerical Solving methods | /r/DifferentialEquations | 2023-07-06

There has been a lot of research in Runge Kutta methods in the last couple decades which resulted in all kind of specialized Runge Kutta methods. You have high order ones, RK methods for stiff problems, embedded RK methods which benefit from adaprive step size control, RK-Nystrom methods for second order Problems, symplectic RK methods which preserve energy (eg. hamiltonian) ando so on. If you are interested in the numerics and the use cases I highly recommend checking out the Julia Libary OrdinaryDiffEq (https://github.com/SciML/OrdinaryDiffEq.jl). If you look into the documentation you find A LOT of implemented RK methods for all kind of use cases.

• Catalyst.jl

  2 422 9.5 Julia

  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

  3 398 6.3 Julia

  Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

  

• Surrogates.jl

  1 314 8.7 Julia

  Surrogate modeling and optimization for scientific machine learning (SciML)

  

• SciMLSensitivity.jl

  2 311 9.5 Julia

  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.

  

• DiffEqBase.jl

  1 297 9.3 Julia

  The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

  

• ComponentArrays.jl

  1 276 7.0 Julia

  Arrays with arbitrarily nested named components.

• DiffEqGPU.jl

  2 267 8.5 Julia

  GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

  

Project mention: 2023 was the year that GPUs stood still | news.ycombinator.com | 2023-12-29

Indeed, and this year we created a system for compiling ODE code not just optimized CUDA kernels but also OneAPI kernels, AMD GPU kernels, and Metal. Peer reviewed version is here (https://www.sciencedirect.com/science/article/abs/pii/S00457...), open access is here (https://arxiv.org/abs/2304.06835), and the open source code is at https://github.com/SciML/DiffEqGPU.jl. The key that the paper describes is that in this case kernel generation is about 20x-100x faster than PyTorch and Jax (see the Jax compilation in multiple ways in this notebook https://colab.research.google.com/drive/1d7G-O5JX31lHbg7jTzz..., extra overhead though from calling Julia from Python but still shows a 10x).

The point really is that while deep learning libraries are amazing, at the end of the day they are DSL and really pull towards one specific way of computing and parallelization. It turns out that way of parallelizing is good for deep learning, but not for all things you may want to accelerate. Sometimes (i.e. cases that aren't dominated by large linear algebra) building problem-specific kernels is a major win, and it's over-extrapolating to see ML frameworks do well with GPUs and think that's the only thing that's required. There are many ways to parallelize a code, ML libraries hardcode a very specific way, and it's good for what they are used for but not every problem that can arise.

• StochasticDiffEq.jl

  1 234 7.8 Julia

  Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

  

• NonlinearSolve.jl

  2 206 9.7 Julia

  High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

  

• RecursiveArrayTools.jl

  3 202 9.4 Julia

  Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications

  

• ReservoirComputing.jl

  1 200 8.5 Julia

  Reservoir computing utilities for scientific machine learning (SciML)

  

• SciMLStyle

  2 195 6.1 Julia

  A style guide for stylish Julia developers

  

Project mention: Julia as a unifying end-to-end workflow language on the Frontier exascale system | news.ycombinator.com | 2023-11-19

• NBodySimulator.jl

  2 124 4.9 Julia

  A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

  

• SymbolicNumericIntegration.jl

  1 113 7.3 Julia

  SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals

  

• ParameterizedFunctions.jl

  1 76 6.5 Julia

  A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

  

• DiffEqDevTools.jl

  3 46 8.5 Julia

  Benchmarking, testing, and development tools for differential equations and scientific machine learning (SciML)

  

• MuladdMacro.jl

  3 45 6.3 Julia

  This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem

  

Project mention: Std: Clamp generates less efficient assembly than std:min(max,std:max(min,v)) | news.ycombinator.com | 2024-01-16

Totally agreed. In Julia we use https://github.com/SciML/MuladdMacro.jl all over the place so that way it's contextual and does not bleed into other functions. fast-math changing everything is just... dangerous.

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Julia scientific-machine-learning related posts

• Julia's latency: Past, present and future

  1 project | /r/Julia | 1 Apr 2023

• Why Fortran is a scientific powerhouse

  2 projects | news.ycombinator.com | 11 Jan 2023

• How much useful are Runge-Kutta methods of order 9 and higher within double-precision arithmetic/floating point accuracy?

  2 projects | /r/Julia | 2 Sep 2022

• Interpolant Coefficients for the BS5 Runge-Kutta method

  1 project | /r/Julia | 11 Aug 2022

• “Why I still recommend Julia”

  11 projects | news.ycombinator.com | 25 Jun 2022

• Why Fortran is easy to learn

  19 projects | news.ycombinator.com | 7 Jan 2022

• Tutorials for Learning Runge-Kutta Methods with Julia?

  5 projects | /r/Julia | 27 Dec 2021
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