Hallo!

I currently work on a software product that interfaces with a hardware device. The hardware device takes a set of 5 Parameters. When the software installs, we are able to generate these 5 parameters for the customers specific hardware to a “close enough” degree of accuracy using basic physics and math calculations for the device. However, i have noticed with my logging that in most cases the customers are having to adjust these parameters up to +- 5% to get to an optimal value.

If instead of just logging these parameter changes, would i be able to feed them into an ML model of some kind which would then be able to learn for which values i generate, need to be adjusted by a small amount?
My ML experience so far is mainly just predictive models using Naive Bayes, a few genetiv algorithms and LSTM models for algo trading. However i am not sure how i should approach this problem so i am interested in any insight.

Thanks!