Abstract

Large-scale nonlinear dynamical systems, such as models of atmospheric hydrodynamics, chemical reaction networks, and electronic circuits, often involve thousands or more interacting components. In order to identify key components in the complex dynamical system as well as to accelerate simulations, model reduction is often desirable. In this work, we develop a new data-driven method utilizing â.,"1-regularization for model reduction of nonlinear dynamical systems, which involves minimal parameterization and has polynomial-time complexity, allowing it to easily handle large-scale systems with as many as thousands of components in a matter of minutes. A primary objective of our model reduction method is interpretability, that is to identify key components of the dynamical system that contribute to behaviors of interest, rather than just finding an efficient projection of the dynamical system onto lower dimensions. Our method produces a family of reduced models that exhibit a trade-off between model complexity and estimation error. We find empirically that our method chooses reduced models with good extrapolation properties, an important consideration in practical applications. The reduction and extrapolation performance of our method are illustrated by applications to the Lorenz model and chemical reaction rate equations, where performance is found to be competitive with or better than state-of-the-art approaches.