Data-driven identification of interpretable reduced-order models using sparse regression

Abstract

Developing physically interpretable reduced-order models (ROMs) is critical as they provide an understanding of the underlying phenomena apart from computational tractability for many chemical processes. In this work, we re-envision the model reduction of nonlinear dynamical systems from the perspective of regression. In particular, we solve a sparse regression problem over a large set of candidate functional forms to determine the structure of the ROM. The method balances model complexity and accuracy by selecting a sparse model that avoids overfitting to accurately represent the system dynamics when subjected to a different input profile. By applying to a hydraulic fracturing process, we demonstrate the ability of the developed models to reveal important physical phenomena such as proppant transport and fracture propagation inside a fracture. It also highlights how a priori knowledge can be incorporated easily into the algorithm and results in accurate ROMs that are used for controller synthesis.