pySRURGS
diffeqpy
| pySRURGS | diffeqpy | |
|---|---|---|
| 1 | 4 | |
| 13 | 496 | |
| - | 1.8% | |
| 0.0 | 7.7 | |
| 10 months ago | about 2 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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pySRURGS
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‘Machine Scientists’ Distill the Laws of Physics from Raw Data
It should also be emphasized that genetic programming is just one approach to program synthesis, i.e. automatically deriving computer programs from data.
You don't have to use genetic/evolutionary algorithms to search the space of functions, it's just the most popular method.
You can even try pure random search if you're feeling particularly lucky:
https://github.com/pySRURGS/pySRURGS
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?
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
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.
randfacts - Python module used to generate random facts
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
atmos-rng - A randomness generator based off of atmospheric noise instead of math to generate numbers, choices, and to shuffle lists.
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Causal.jl - Causal.jl - A modeling and simulation framework adopting causal modeling approach.
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]
Evolution_simulation - using ursina, I have made a Evolution simulation. To move the screen use [w,s,d,a] keys to move through the x and y directions and use the [e,r] values to move through the z axis. Use the sliders to control the death and birth rate of the simulation. Don't be afraid to change the code or to reload the simulation multiple times.
csvzip - A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
FunctionalModels.jl - Equation-based modeling and simulations in Julia
PySR - High-Performance Symbolic Regression in Python and Julia