pySRURGS
Causal.jl
| pySRURGS | Causal.jl | |
|---|---|---|
| 1 | 2 | |
| 13 | 109 | |
| - | - | |
| 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
Causal.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.
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Should I switch over completely to Julia from Python for numerical analysis/computing?
ModelingToolkit is not equivalent to Simulink. Simulink is a causal modeling framework with a code-based underpinning. The closest to Simulnik would actually be Causal.jl, which is a really nice package in its own right, quite fast, and has a really expansive feature-set. For causal modeling in the form of Simulink, it is definitely a cool package to look into.
What are some alternatives?
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
randfacts - Python module used to generate random facts
casadi - CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
atmos-rng - A randomness generator based off of atmospheric noise instead of math to generate numbers, choices, and to shuffle lists.
OMJulia.jl - Julia scripting OpenModelica interface
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.
ScottishTaxBenefitModel.jl - A tax-benefit model for Scotland
FunctionalModels.jl - Equation-based modeling and simulations in Julia
Modia.jl - Modeling and simulation of multidomain engineering systems
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
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.