Julia partial-differential-equations

Open-source Julia projects categorized as partial-differential-equations Edit details

Top 10 Julia partial-differential-equation Projects

  • DifferentialEquations.jl

    Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components

    Project mention: From Common Lisp to Julia | news.ycombinator.com | 2022-09-06

    https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.

    Also, if you take a look at a tutorial, say the tutorial video from 2018,

  • DiffEqFlux.jl

    Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

    Project mention: Jax vs. Julia (Vs PyTorch) | news.ycombinator.com | 2022-05-04
  • SonarLint

    Clean code begins in your IDE with SonarLint. Up your coding game and discover issues early. SonarLint is a free plugin that helps you find & fix bugs and security issues from the moment you start writing code. Install from your favorite IDE marketplace today.

  • NeuralPDE.jl

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

    Project mention: from Wolfram Mathematica to Julia | reddit.com/r/Julia | 2022-05-26

    PDE solving libraries are MethodOfLines.jl and NeuralPDE.jl. NeuralPDE is very general but not very fast (it's a limitation of the method, PINNs are just slow). MethodOfLines is still somewhat under development but generates quite fast code.

  • Gridap.jl

    Grid-based approximation of partial differential equations in Julia

    Project mention: Best free/open source CAS ? | reddit.com/r/MechanicalEngineering | 2022-06-25

    Another I've been working on learning is Julia, which aims to use a syntax very similar to how you'd write it mathematically, and I like being able to include units in calculations using the unitful.jl package, and there are FEM packages available like Gridap.

  • ApproxFun.jl

    Julia package for function approximation

  • DiffEqOperators.jl

    Linear operators for discretizations of differential equations and scientific machine learning (SciML)

    Project mention: Julia 1.7 has been released | news.ycombinator.com | 2021-11-30

    >I hope those benchmarks are coming in hot

    M1 is extremely good for PDEs because of its large cache lines.

    https://github.com/SciML/DiffEqOperators.jl/issues/407#issue...

    The JuliaSIMD tools which are internally used for BLAS instead of OpenBLAS and MKL (because they tend to outperform standard BLAS's for the operations we use https://github.com/YingboMa/RecursiveFactorization.jl/pull/2...) also generate good code for M1, so that was giving us some powerful use cases right off the bat even before the heroics allowed C/Fortran compilers to fully work on M1.

  • SciMLBenchmarks.jl

    Benchmarks for scientific machine learning (SciML) software, scientific AI, and (differential) equation solvers

    Project mention: Why Fortran is easy to learn | news.ycombinator.com | 2022-01-07

    > But in the end, it's FORTRAN all the way down. Even in Julia.

    That's not true. None of the Julia differential equation solver stack is calling into Fortran anymore. We have our own BLAS tools that outperform OpenBLAS and MKL in the instances we use it for (mostly LU-factorization) and those are all written in pure Julia. See https://github.com/YingboMa/RecursiveFactorization.jl, https://github.com/JuliaSIMD/TriangularSolve.jl, and https://github.com/JuliaLinearAlgebra/Octavian.jl. And this is one part of the DiffEq performance story. The performance of this of course is all validated on https://github.com/SciML/SciMLBenchmarks.jl

  • Scout APM

    Truly a developer’s best friend. Scout APM is great for developers who want to find and fix performance issues in their applications. With Scout, we'll take care of the bugs so you can focus on building great things 🚀.

  • DiffEqBase.jl

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

    Project mention: Simulating a simple circuit with the ModelingToolkit | reddit.com/r/Julia | 2022-06-29
  • FourierFlows.jl

    Tools for building fast, hackable, pseudospectral partial differential equation solvers on periodic domains

  • MethodOfLines.jl

    Automatic Finite Difference PDE solving with Julia SciML

    Project mention: from Wolfram Mathematica to Julia | reddit.com/r/Julia | 2022-05-26

    PDE solving libraries are MethodOfLines.jl and NeuralPDE.jl. NeuralPDE is very general but not very fast (it's a limitation of the method, PINNs are just slow). MethodOfLines is still somewhat under development but generates quite fast code.

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). The latest post mention was on 2022-09-06.

Julia partial-differential-equations related posts

Index

What are some of the best open-source partial-differential-equation projects in Julia? This list will help you:

Project Stars
1 DifferentialEquations.jl 2,332
2 DiffEqFlux.jl 720
3 NeuralPDE.jl 646
4 Gridap.jl 443
5 ApproxFun.jl 440
6 DiffEqOperators.jl 264
7 SciMLBenchmarks.jl 225
8 DiffEqBase.jl 203
9 FourierFlows.jl 128
10 MethodOfLines.jl 84
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