pySRURGS
FunctionalModels.jl
pySRURGS | FunctionalModels.jl | |
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1 | 1 | |
13 | 113 | |
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0.0 | 0.0 | |
10 months ago | over 2 years ago | |
Python | Julia | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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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
FunctionalModels.jl
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‘Machine Scientists’ Distill the Laws of Physics from Raw Data
The thing to watch in the space of Simulink/Modelica is https://github.com/SciML/ModelingToolkit.jl . It's an acausal modeling system similar to Modelica (though extended to things like SDEs, PDEs, and nonlinear optimization), and has a standard library (https://github.com/SciML/ModelingToolkitStandardLibrary.jl) similar to the MSL. There's still a lot to do, but it's pretty functional at this point. The two other projects to watch are FunctionalModels.jl (https://github.com/tshort/FunctionalModels.jl, which is the renamed Sims.jl), which is built using ModelingToolkit.jl and puts a more functional interface on it. Then there's Modia.jl (https://github.com/ModiaSim/Modia.jl) which had a complete rewrite not too long ago, and in its new form it's fairly similar to ModelingToolkit.jl and the differences are more in the details. For causal modeling similar to Simulink, there's Causal.jl (https://github.com/zekeriyasari/Causal.jl) which is fairly feature-complete, though I think a lot of people these days are going towards acausal modeling instead so flipping Simulink -> acausal, and in that transition picking up Julia, is what I think is the most likely direction (and given MTK has gotten 40,000 downloads in the last year, I think there's good data backing it up).
And quick mention to bring it back to the main thread here, the DataDrivenDiffEq symbolic regression API gives back Symbolics.jl/ModelingToolkit.jl objects, meaning that the learned equations can be put directly into the simulation tools or composed with other physical models. We're really trying to marry this process modeling and engineering world with these "newer" AI tools.
What are some alternatives?
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
randfacts - Python module used to generate random facts
Modia.jl - Modeling and simulation of multidomain engineering systems
atmos-rng - A randomness generator based off of atmospheric noise instead of math to generate numbers, choices, and to shuffle lists.
PySR - High-Performance Symbolic Regression in Python and Julia
Causal.jl - Causal.jl - A modeling and simulation framework adopting causal modeling approach.
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.
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