fractalrabbit
Causal.jl
| fractalrabbit | Causal.jl | |
|---|---|---|
| 1 | 2 | |
| 136 | 109 | |
| 0.7% | - | |
| 2.7 | 0.0 | |
| 11 days ago | over 2 years ago | |
| Java | Julia | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fractalrabbit
Causal.jl
-
‘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.
-
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?
ape-ecs - Entity-Component-System library for JavaScript.
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.
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.
OMJulia.jl - Julia scripting OpenModelica interface
ScottishTaxBenefitModel.jl - A tax-benefit model for Scotland
Modia.jl - Modeling and simulation of multidomain engineering systems
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.
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
MomentClosure.jl - Tools to generate and study moment equations for any chemical reaction network using various moment closure approximations
PySR - High-Performance Symbolic Regression in Python and Julia
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
pySRURGS - Symbolic regression by uniform random global search