> The nonlinearities are the bread and butter of modern ML models
I guess I didn't get my point across. What I meant was that pedal settings tend to be non-linear with multiple sweet spots (which often depend on the guitar and amp) so you shouldn't just do a linear range from 1-10^N (where N is the number of knobs) for training data, as someone else had suggested. Moreover, there are also dependencies on the impedance chain, gain structure, feedback, reflections, etc., which seem well-suited to circuit and physical modeling. Digital pedals, as I note, are largely software anyway so it doesn't make sense to me to try to model them with ML any more than it does to model Microsoft Word using ML (though I'm sure someone has tried.)
In general ML seems most useful when you don't have good analytical models - but in the case of circuits and software we have very good analytical models.
That's fair, and true! But one interesting thing about ML models is that they're often much more performant. For example, it's relatively expensive to evaluate analog circuits digitally. An ML model that can do it on a raspi with no delay and no quality loss is interesting, to me at least.
I guess I didn't get my point across. What I meant was that pedal settings tend to be non-linear with multiple sweet spots (which often depend on the guitar and amp) so you shouldn't just do a linear range from 1-10^N (where N is the number of knobs) for training data, as someone else had suggested. Moreover, there are also dependencies on the impedance chain, gain structure, feedback, reflections, etc., which seem well-suited to circuit and physical modeling. Digital pedals, as I note, are largely software anyway so it doesn't make sense to me to try to model them with ML any more than it does to model Microsoft Word using ML (though I'm sure someone has tried.)
In general ML seems most useful when you don't have good analytical models - but in the case of circuits and software we have very good analytical models.