new to data science/ML but experienced programmer in C/python for engineering/electronics.
I am trying to predict the temperature of a rotor inside an electric motor. I have a lot of data to feed into the model that should correlate to the instantaneous loss in the rotor. I also have data of the instantaneous coolant in the stator of the motor. We do not have a solid working model of the losses or the thermal circuit from the rotor to the coolant so would like to use ML to predict the final rotor temperature.
The issue I am having is that the rotor is thermally isolated and thermally capacitive, and therefore accumulates temperature over time from a known starting temperature.
most of the ML literature I have read about rely on the output to be instantaneously linked to the input dataset and not have this cumulative element to the relationship, can someone suggest an approach or technique to read around that could help with this situation?