Modia.jl
VCVRackLunettaModula
Modia.jl | VCVRackLunettaModula | |
---|---|---|
4 | 1 | |
318 | 16 | |
0.6% | - | |
6.7 | 10.0 | |
6 months ago | over 1 year ago | |
Julia | C++ | |
MIT License | GNU General Public License v3.0 or later |
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Modia.jl
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An open source, educational, low-cost modern analog computer
For circuits a lot of them are represented by differential-algebraic equations (DAEs) and require modeling tools in order to handle the high differential index of the systems. This is the reason why they are typically handled via acausal modeling systems which can do index reduction. For Julia, this is the ModelingToolkit portion of the SciML ecosystem (https://docs.sciml.ai/ModelingToolkit/stable/), and some modeling tools like https://github.com/ModiaSim/Modia.jl and OpenModelica front-ends https://github.com/OpenModelica/OMJulia.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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Julia Receives DARPA Award to Accelerate Electronics Simulation by 1,000x
Maybe of interest in that context:
https://github.com/ModiaSim/Modia.jl
The authors of that tool have a strong background in modeling and simulation of differential algebraic equations. Not so much in designing DSLs, though, so there maybe some technical oddities. But I expect the simulation aspect to be quite decent.
VCVRackLunettaModula
What are some alternatives?
Verilog.jl - Verilog for Julia
svls - SystemVerilog language server
Automa.jl - A julia code generator for regular expressions
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
Causal.jl - Causal.jl - A modeling and simulation framework adopting causal modeling approach.
PySR - High-Performance Symbolic Regression in Python and Julia
FunctionalModels.jl - Equation-based modeling and simulations in Julia
OMJulia.jl - Julia scripting OpenModelica interface
RecursiveFactorization.jl
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
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