RECOVERING MODEL INVARIANTS FROM SIMULATION TRACES WITH PETRI NET ANALYSIS TECHNIQUES

Modern modeling frameworks allow us to generate and simulate discrete event system models of great complexity. The use of existing environments, and the use, calibration, and configuration of existing submodels to build large models in a productive manner can rise the question what really happens in a simulation run for a particular experiment. In this paper, we describe ways to make use of invariant analysis techniques that originate from the theory of Petri nets, to be applied in the broader setting of invariant identification from simulation traces. The key idea is to provide feedback to a modeler on constraints that hold for the observed behavior of a model in a simulation run.