Causal.jl VS casadi

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.

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.

Interface for several solvers and integrators.

Howdy! Collocation methods can be... tricky. For NMPC control of vehicles, success has been had using direct multiple shooting. Also easier to implement and more intuitive. In fact, this example from the GH is pretty instructive: https://github.com/casadi/casadi/blob/master/docs/examples/python/race_car.py

JuMP.jl (Julia) or casADi (Python) are good choices.

Python is not mature in this area. If you ask Google what Simulink for Python is, you get responses that point to dead libraries that were never feature complete and slow. The absolute closest is CASADI which is nice for some things but doesn't even have a true causal modeling interface and is mostly abandoned by the developers (they put a patch in every now and then, but just look at the commit graph), and it's slow compared to the Julia tools, so much so that PyBAMM is interfacing with ModelingToolkit.jl in Julia for a performance boost. Python is not the place to be for causal/acausal modeling or controls. Anyone who is saying "Python is mature" here is saying it in the abstract and not in the context of your actual question. Yes, Python has web development frameworks. No it does not have good libraries for tons of areas (control, acausal modeling, pharmacometrics, etc.).