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Diffeqpy Alternatives
Similar projects and alternatives to diffeqpy
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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
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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.
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auto-07p
AUTO is a publicly available software for continuation and bifurcation problems in ordinary differential equations originally written in 1980 and widely used in the dynamical systems community.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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DiffEqBase.jl
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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csvzip
A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
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DiffEqSensitivity.jl
Discontinued 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]
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
diffeqpy discussion
diffeqpy reviews and mentions
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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
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Stats
SciML/diffeqpy is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of diffeqpy is Python.