‘Machine Scientists’ Distill the Laws of Physics from Raw Data

1. PySR

   1 8 2,555 9.6 Python

   High-Performance Symbolic Regression in Python and Julia

   I found it curious that one of the implementations of symbolic regression (the "machine scientist" referenced in the article) is a Python wrapper on Julia: https://github.com/MilesCranmer/PySR

   I don't think I've seen a Python wrapper on Julia code before.

2. SaaSHub

   www.saashub.com featured

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

   

3. ModelingToolkit.jl

   2 16 1,452 9.9 Julia

   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

   

4. ModelingToolkitStandardLibrary.jl

   3 2 126 9.4 Julia

   A standard library of components to model the world and beyond

   

5. FunctionalModels.jl

   4 1 112 0.0 Julia

   Equation-based modeling and simulations in Julia

6. Modia.jl

   5 5 323 6.7 Julia

   Modeling and simulation of multidomain engineering systems

   

7. Causal.jl

   6 2 115 0.0 Julia

   Causal.jl - A modeling and simulation framework adopting causal modeling approach.

8. pySRURGS

   7 1 13 0.0 Python

   Symbolic regression by uniform random global search

   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

9. diffeqpy

   8 4 549 7.3 Python

   Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

   

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