Possibly similar question: Is it ok to collect data using algorithm to train another?
I have a model that accurately describes an underlying physical, complex, system. The model is basically a set of ODEs based on the physics of the system, validated against measurements. When a system perturbation occurs, I can run thousands of simulations to assess if the new system status is secure or not. This is basically a classification procedure (yes/no).
This procedure is very time consuming and has to be performed in real-time (thus, requiring huge computational resources). There are thousands of possible perturbations and infinite number of initial points. The same perturbation from 1 initial point can lead to stable system, while from another, to an unstable.
My question is:
Is it possible to use data generated by a huge number of simulations to train a classification algorithm to perform this detection online? What are the considerations when using simulated data to train an algorithm that will then be used online with real data (expect from the obvious that the simulation needs to be very very accurate)? Any references to such examples?
I apologise if this is a basic question. I am new to data science techniques with a more physics/engineering background.