So I've this model that simulates an ecosystem and outputs its attributes, like its chemistry, temperature etc. There are lots of input parameters to the model.
My job is to write a program to figure out the values of those parameters automatically using machine learning techniques. i.e to make a guess, run the simulation, then check the results against actual historically observed field data. If the results are very close to the field data, then the parameters are probably correct. If they are off, then I make some adjustment of the parameters and run again. Every parameter has a default value, and can be varied only by +\- 30% of its default value.
There around 30 input parameters to the simulation. However, only 8-10 are candidates for estimation. The simulation takes around 5 minutes to run.
Is this a Parameter estimation problem? I know of few algorithms meant for parameter estimation, like MCMC & Simulated annealing. Are they suitable in this case?
I could easily come up with a naive implementation for varying the parameters' values. Could someone guide/suggest me an approach to come up with an efficient solution?