I'm going to be fully honest, I'm very new to data science. I am a Mechanical Engineering major and we didn't really do much of that beyond basic statistics.
I'll explain what I'm attempting to do. I have an analytical model that predicts heat transfer and returns a temperature distribution along a given cross-section given about a dozen parameters. I also have experimental data that I would like to compare against this analytical model in order to find one specific parameter for this model, where all others are assumed to be given. Unfortunately, this model is not "separable" (I don't know if that's the right word), in that I can't make a simplified model that would only require that one parameter input.
Basically, I am iterating through the range of possible values for this parameter and calculating the predicted temperature distribution for each possible value, then comparing it against the experimental data and calculating the RSME for each possible value for this specific parameter. This "lowest RSME" approach has given me a value for this parameter that is in line with other research that has been done on this, but I would like to somehow quantify the uncertainty in this value.
I am open to any and all suggestions. Whether it be if my "lowest RSME" approach isn't the best as compared with some other calculation, or how to go about finding this uncertainty.
