Hello All,
Although this question is a bit more directed at JMP employees, other users might have additional information as well.
I'm curious if JMP plans to implement any physics-informed neural networks (PINNs) in their NN modeling platform. As a physicist (in industry, not academia), much of the data I analyze has underlying physical constraints that are hard to capture within a standard JMP data table. And although I appreciate the hard work JMP has put into their predictive modeling platforms like XGBoost or NN, or their Pytorch add-in, it's often hard to extract the physical drivers/limits when modeling the data. Sure, in an ideal situation, I can generate my own formula to model data, but in real world situations, we don't have harmonic oscillators that govern the response, it's so much more "messy" than that, and we don't have model formula(s) to work with.
It would be really cool to add some kind of physics-informed option where a penalization factor can be included in a model so that if a fundamental physical law is violated, that pathway is penalized and vice-versa. Better yet, include multiple physics-informed penalization factors. The statistics is great and all, but with nearly every data set out there, there are some kind of physical laws that apply.
Just curious.
Thanks!,
DS
