Easier with Julia, though, because the ODE and PDE support is better and you have better / simpler notation. Performance will be better as well, but for simple problems you can't tell.
The cool thing about analog computers (or digital approximations or simulations) is that since they are actually integrating (storing charge in a capacitor as the voltage varies) or actually differentiating (current through a capacitor varies with the voltage differential/rate of change) they can calculate what would be very complex to do on paper.
Or more plainly, if the rate that the water flows out of the drain of the bathtub depends on the water pressure above which depends on the depth of the water in the tub, you can study all the math involved (what you did in calculus) or you can just put water in the tub and set your stopwatch (analog computer)
this is basically all that's involved inside a music synthesizer. The complexity comes from stacking together a variety of different concepts once you understand that the concepts are stackable (like when they taught you about arithmetic being commutative, associative, distributive, reflexive, etc)
So in my top level I mentioned Simulink as a nice path for students to learn modeling circuits, and in a way then could use this path to simulate what an Analog computer such as that in the original link is running.
My early forays into numerical simulation and linear systems modeling were thru Simulink, and I've built models of some very big systems in Simulink and used them in the design process of various things- typically vehicles, automotive systems; modeling powertrains, electro-hydraulic controls, EV drive cycle range simulation, etc. That was all many years ago.
I then went down the path of learning a bit of OpenModelica and as I was intrigued by the acausal approach and the open source aspect. Like anything the learning curve was steep (steeper than simulink) and I never got past a lot of clunkiness.
Then I discovered Julia a few years ago and even though I've moved on to other work, I've followed how people using Julia are modeling and solving closely, and what Chris says about how building acausal systems can be easier, and the benefits seen in ModelingToolkit.jl combined with the full Julia ecosystem all rings so true. I'm really excited to eventually see a full on acausal, block-based, yet easily extendable Simulink competitor sprout from Julia devs because as it does it will be the killer app for the language for a lot of engineers, IMO.
Funny enough, now Mathworks implements an acausal modeling approach in the Simulink GUI domain with their 'SimScape' add-on, but I have not gotten a chance to use it.
Anyway, yeah I need to get a project going to utilize some of the latest happenings in Julia modeling, and maybe find a way to contribute. Cheers to you and the other devs, Chris!
VCV Rack cannot do Zero Delay Feedback because each cable adds one sample of delay. You can do it inside your module, of course.
It would be crude and inefficient but you also could use spice and rig up op amps just like a physical analog computer if you just want to learn or if spice is your comfort zone. Or if you want to add additional analog circuitry or spice models. It’s the 21st century so it will be fast enough computationally for small to medium sized models.
If you are a student and want to learn a bit more about how operational amplifiers, resistors, and capacitors alone can solve differential equations... you can get an educational license for Matlab for nothing and Simulink can emulate all the analog circuitry you want and more.
Another approach might be using the Eurorack simulation software VCV Rack - If there isn't an set of modules you can install that do basic gain, integrators, multipliers, comparators, etc then it would be very easy for someone to write one.