DiffEqSensitivity.jl
diffeqpy
| DiffEqSensitivity.jl | diffeqpy | |
|---|---|---|
| 2 | 4 | |
| 184 | 496 | |
| - | 1.8% | |
| 9.5 | 7.7 | |
| almost 2 years ago | about 2 months ago | |
| Julia | Python | |
| GNU General Public License v3.0 or later | MIT License |
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DiffEqSensitivity.jl
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[R] New directions in Neural Differential Equations
One reason is that it's not robust and has some odd counter example cases that can come up where the ODE solver is able to converge rapidly on the original problem but not so rapidly in the integral sense on the derivative values. One such case showed up in this issue, which was the impetus for the change in the forward-mode sense, while the reverse sense was changed in testing with direct quadratures (which will be mentioned in a bit).
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Odd Behavior: Neural network hybrid differential equation example
Thanks for letting us know. The fix is in https://github.com/SciML/DiffEqSensitivity.jl/pull/386 and hopefully that'll get released today.
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?
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
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
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
csvzip - A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
StochasticDiffEq.jl - Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
PySR - High-Performance Symbolic Regression in Python and Julia
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
python-bigsimr