SaaSHub helps you find the best software and product alternatives Learn more →
Top 23 Julia scientificmachinelearning Projects

DifferentialEquations.jl
Multilanguage suite for highperformance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differentialalgebraic equations (DAEs), and more in Julia.

ModelingToolkit.jl
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physicsinformed machine learning and automated transformations of differential equations

InfluxDB
Power RealTime Data Analytics at Scale. Get realtime insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in realtime with unbounded cardinality.

NeuralPDE.jl
PhysicsInformed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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/ghpages/v5.7.0/assets/Manifest.toml). You just ]instantiate folder_of_manifest. Or you can use the Project.toml.

DiffEqFlux.jl
Prebuilt implicit layer architectures with O(1) backprop, GPUs, and stiff+nonstiff DE solvers, demonstrating scientific machine learning (SciML) and physicsinformed machine learning methods

Optimization.jl
Mathematical Optimization in Julia. Local, global, gradientbased and derivativefree. Linear, Quadratic, Convex, MixedInteger, 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  20230518Interesting 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
High performance ordinary differential equation (ODE) and differentialalgebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
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, RKNystrom 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
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPUparallelized, and O(1) solvers in open source software.

WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easytouse, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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


SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimizethendiscretize, discretizethenoptimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

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


DiffEqGPU.jl
GPUacceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
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 20x100x faster than PyTorch and Jax (see the Jax compilation in multiple ways in this notebook https://colab.research.google.com/drive/1d7GO5JX31lHbg7jTzz..., 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 problemspecific kernels is a major win, and it's overextrapolating 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
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

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


NonlinearSolve.jl
Highperformance and differentiationenabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and NewtonKrylov support.

Project mention: Julia as a unifying endtoend workflow language on the Frontier exascale system  news.ycombinator.com  20231119

NBodySimulator.jl
A differentiable simulator for scientific machine learning (SciML) with Nbody problems, including astrophysical and molecular dynamics

SymbolicNumericIntegration.jl
SymbolicNumericIntegration.jl: SymbolicNumerics for Solving Integrals

ParameterizedFunctions.jl
A simple domainspecific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications

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

MuladdMacro.jl
This package contains a macro for converting expressions to use muladd calls and fusedmultiplyadd (FMA) operations for highperformance 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  20240116Totally 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. fastmath changing everything is just... dangerous.

SaaSHub
SaaSHub  Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Julia scientificmachinelearning related posts
 Julia's latency: Past, present and future
 Why Fortran is a scientific powerhouse
 How much useful are RungeKutta methods of order 9 and higher within doubleprecision arithmetic/floating point accuracy?
 Interpolant Coefficients for the BS5 RungeKutta method
 “Why I still recommend Julia”
 Why Fortran is easy to learn
 Tutorials for Learning RungeKutta Methods with Julia?

A note from our sponsor  SaaSHub
www.saashub.com  13 Apr 2024
Index
What are some of the best opensource scientificmachinelearning projects in Julia? This list will help you:
Project  Stars  

1  DifferentialEquations.jl  2,737 
2  ModelingToolkit.jl  1,330 
3  NeuralPDE.jl  891 
4  DiffEqFlux.jl  833 
5  Optimization.jl  652 
6  OrdinaryDiffEq.jl  499 
7  Catalyst.jl  419 
8  DataDrivenDiffEq.jl  397 
9  Surrogates.jl  309 
10  SciMLSensitivity.jl  305 
11  DiffEqBase.jl  295 
12  ComponentArrays.jl  275 
13  DiffEqGPU.jl  267 
14  StochasticDiffEq.jl  233 
15  RecursiveArrayTools.jl  197 
16  ReservoirComputing.jl  197 
17  NonlinearSolve.jl  191 
18  SciMLStyle  172 
19  NBodySimulator.jl  124 
20  SymbolicNumericIntegration.jl  113 
21  ParameterizedFunctions.jl  76 
22  DiffEqDevTools.jl  46 
23  MuladdMacro.jl  45 