Integrating Knowledge-Driven and Data-Driven Approaches to Modeling

Abstract

In this paper, we present a modeling framework that integrates the knowledge-based theoretical approach to modeling with the data-driven empirical modeling of dynamic systems. The framework allows for integration of modeling knowledge specific to the domain of interest in the process of model induction from measured data. The knowledge is organized around the central notion of basic processes in the domain, their models, and includes guidelines for combining models of individual processes into a model of the entire observed system. We present a method for automated translation of the knowledge into the operational form of grammars that constrain the space of candidate models considered during the induction process. The developed framework is applied to two tasks of modeling dynamic systems from noisy measurement data in the domains of population and hydro dynamics.