Pde

Top 21 Pde Open-Source Projects

  • processing

    Source code for the Processing Core and Development Environment (PDE)

    Project mention: Let's compile like it's 1992 | news.ycombinator.com | 2024-02-26

    Would processing[0] be a good fit? It's designed to be easy to use and learn but powerful enough for professional use. Very quick to get cool stuff moving on a screen and the syntax is Java with a streamlined editing environment.

    [0] https://processing.org/

  • 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.

  • InfluxDB

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

  • deepxde

    A library for scientific machine learning and physics-informed learning

  • neuraloperator

    Learning in infinite dimension with neural operators.

    Project mention: Learn in Infinite Dimensions | news.ycombinator.com | 2024-01-05
  • 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

  • NeuralPDE.jl

    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.

  • SciMLTutorials.jl

    Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  • scikit-fem

    Simple finite element assemblers

  • DiffEqBase.jl

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

  • SciMLBenchmarks.jl

    Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

    Project mention: Can Fortran survive another 15 years? | news.ycombinator.com | 2023-05-01

    What about the other benchmarks on the same site? https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Bio/BCR/ BCR takes about a hundred seconds and is pretty indicative of systems biological models, coming from 1122 ODEs with 24388 terms that describe a stiff chemical reaction network modeling the BCR signaling network from Barua et al. Or the discrete diffusion models https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Jumps/Dif... which are the justification behind the claims in https://www.biorxiv.org/content/10.1101/2022.07.30.502135v1 that the O(1) scaling methods scale better than O(log n) scaling for large enough models? I mean.

    > If you use special routines (BLAS/LAPACK, ...), use them everywhere as the respective community does.

    It tests with and with BLAS/LAPACK (which isn't always helpful, which of course you'd see from the benchmarks if you read them). One of the key differences of course though is that there are some pure Julia tools like https://github.com/JuliaLinearAlgebra/RecursiveFactorization... which outperform the respective OpenBLAS/MKL equivalent in many scenarios, and that's one noted factor for the performance boost (and is not trivial to wrap into the interface of the other solvers, so it's not done). There are other benchmarks showing that it's not apples to apples and is instead conservative in many cases, for example https://github.com/SciML/SciPyDiffEq.jl#measuring-overhead showing the SciPyDiffEq handling with the Julia JIT optimizations gives a lower overhead than direct SciPy+Numba, so we use the lower overhead numbers in https://docs.sciml.ai/SciMLBenchmarksOutput/stable/MultiLang....

    > you must compile/write whole programs in each of the respective languages to enable full compiler/interpreter optimizations

    You do realize that a .so has lower overhead to call from a JIT compiled language than from a static compiled language like C because you can optimize away some of the bindings at the runtime right? https://github.com/dyu/ffi-overhead is a measurement of that, and you see LuaJIT and Julia as faster than C and Fortran here. This shouldn't be surprising because it's pretty clear how that works?

    I mean yes, someone can always ask for more benchmarks, but now we have a site that's auto updating tons and tons of ODE benchmarks with ODE systems ranging from size 2 to the thousands, with as many things as we can wrap in as many scenarios as we can wrap. And we don't even "win" all of our benchmarks because unlike for you, these benchmarks aren't for winning but for tracking development (somehow for Hacker News folks they ignore the utility part and go straight to language wars...).

    If you have a concrete change you think can improve the benchmarks, then please share it at https://github.com/SciML/SciMLBenchmarks.jl. We'll be happy to make and maintain another.

  • BifurcationKit.jl

    A Julia package to perform Bifurcation Analysis

    Project mention: auto-07p VS BifurcationKit.jl - a user suggested alternative | libhunt.com/r/auto-07p | 2024-02-11

    A Julia alternative with methods for automatic bifurcation diagrams. I can work for very large systems.

  • nvim-config

    Generalized and Personalized (by Alexis12119)

    Project mention: Throw me your nvim config for windows 11. | /r/neovim | 2023-06-28

    Here's my config for windows 10, but I think it'll also work on windows 11. Hope it helps.

  • BOUT-dev

    BOUT++: Plasma fluid finite-difference simulation code in curvilinear coordinate systems

  • pyclaw

    PyClaw is a Python-based interface to the algorithms of Clawpack and SharpClaw. It also contains the PetClaw package, which adds parallelism through PETSc.

    Project mention: 2 Dimensional DG for advection equation | /r/CFD | 2023-04-13
  • MethodOfLines.jl

    Automatic Finite Difference PDE solving with Julia SciML

  • nvim

    neovim lua cfg (by danielnehrig)

  • finite-element-networks

    Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022

  • JFVM.jl

    A simple finite volume tool for Julia

    Project mention: Blog-post: using Julia to efficiently solve a partial differential equation | /r/Julia | 2023-06-21
  • rodin

    Modern C++17 finite element method and shape optimization framework.

  • CProcessing

    Processing C++ Edition (by maksmakuta)

  • curve-shortening-demo

    Visualize curve shortening flow in your browser.

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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 2024-02-26.

Pde related posts

Index

What are some of the best open-source Pde projects? This list will help you:

Project Stars
1 processing 6,443
2 DifferentialEquations.jl 2,729
3 deepxde 2,265
4 neuraloperator 1,717
5 ModelingToolkit.jl 1,321
6 NeuralPDE.jl 891
7 SciMLTutorials.jl 705
8 scikit-fem 422
9 DiffEqBase.jl 291
10 SciMLBenchmarks.jl 289
11 BifurcationKit.jl 282
12 nvim-config 214
13 BOUT-dev 165
14 pyclaw 150
15 MethodOfLines.jl 148
16 nvim 95
17 finite-element-networks 60
18 JFVM.jl 42
19 rodin 34
20 CProcessing 23
21 curve-shortening-demo 21
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SaaSHub helps you find the best software and product alternatives
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