auto-07p
diffeqpy
| auto-07p | diffeqpy | |
|---|---|---|
| 2 | 4 | |
| 113 | 497 | |
| 1.8% | 2.0% | |
| 7.8 | 7.7 | |
| 25 days ago | about 2 months ago | |
| Fortran | Python | |
| - | MIT License |
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auto-07p
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auto-07p VS BifurcationKit.jl - a user suggested alternative
2 projects | 11 Feb 2024
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Error: multiple definitions of block data
The odepack.o is from a .f file and homcont.o is from a .f90. I'm not sure how to fix this error. I can't edit the homcont.f90 file, but I can edit odepack.f. Can someone help me with this? Thanks :)
diffeqpy
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How Julia ODE Solve Compile Time Was Reduced From 30 Seconds to 0.1
With Python you have to write packages in some other language anyways, so you might as well do that with Julia. One of the reasons for getting all of this precompilation going is to eventually ship precompiled system images with things like https://github.com/SciML/diffeqpy, effectively using Julia as a replacement for where C/Fortran is traditionally used there. If I can make that pipeline smooth, then I think Julia as a Python package building source will be a good option for a lot of folks. Right now it's a very manual, but it could easily improve with a bit of tooling.
- ‘Machine Scientists’ Distill the Laws of Physics from Raw Data
- Is it possible to create a Python package with Julia and publish it on PyPi?
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Julia vs R/Python
10-100x speed increase was not an exaggeration for me. With julia I was able to run things quickly on my own machine which I had been running on a compute cluster. I agree that numba could be just as fast as julia. I also just saw that you can run that DE library from julia that I like so much from python using this package. https://github.com/SciML/diffeqpy
What are some alternatives?
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)
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.
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
csvzip - A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
PySR - High-Performance Symbolic Regression in Python and Julia