CyberGene
Pete14 AI (or Machine Learning, to be strict) can help in creating better coefficients/curves/tables (or whatever they call these internal tuning parameters) to try to approximate the original recorded samples as close as possible but it can’t help in making faster computers 😃 I don’t believe we will ever have computers that are fast enough to run a detailed finite element model of a piano in real time.
I remember my work as a beta tester on one of the Pianoteq models. (My NDA has expired long ago). Philippe would prepare these internal calibrations and would give it to us. People would comment, record, discuss. Personally I used to create comparative recordings between Pianoteq and Vintage D (that’s what I had at the time). I would say something like: see how this phrase accentuates metallic overtones in PT and how nicely it blends in Vintage D. Philippe would then do another internal re-configuration based on beta testers feedback. Indeed, sometimes he addressed my comments but then something else got wrong. And in his words (not literal quote, but along the lines of): yes, it’s tricky because adjusting one of these (internal) params changes many things at a time. It’s also when I lost faith in PT. The entire beta process was just moving in circles and it never gradually went better, only changed certain aspects at the expense of others. This might be a subjective understanding and I may be biased but my participation was valuable and they invited me for a few other models but I declined politely.
ML can help in such a process by first learning when a piano sound and behavior is close enough to another one and then apply combinatorial tweaking of all the internal params iteratively until they approach the target sound, rather than relying on beta testers and one man who tweaks some knobs (figuratively) to address comments.