Retrospective-cost-based model refinement for system emulation and subsystem identification

We consider the problem of data-based model refinement, where we assume the availability of an initial model, which may incorporate both physical laws and empirical observations. With this initial model as a starting point, our goal is to use additional measurements to refine the model. In particular, components of the model that are poorly modeled can be updated, thereby resulting in a higher fidelity model. We consider two special cases, namely, system emulation and subsystem identification. In the former case, the main system is assumed to be uncertain and we seek an estimate of the unknown subsystem that allows the overall model to approximate the true system. In this case, there is no expectation that the constructed subsystem model approximates the unknown subsystem. In the latter case, we assume that the main system is accurately modeled and we seek an estimate of the unknown subsystem that approximates the unknown subsystem.