Data-driven prediction of multistable systems from sparse measurements

Abstract

We develop a data-driven method, based on semi-supervised classification, to predict the asymptotic state of multistable systems when only sparse spatial measurements of the system are feasible. Our method predicts the asymptotic behavior of an observed state by quantifying its proximity to the states in a precomputed library of data. To quantify this proximity, we introduce a sparsity-promoting metric-learning (SPML) optimization, which learns a metric directly from the precomputed data. The resulting metric has two important properties: (i) It is compatible with the precomputed library, and (ii) It is computable from sparse measurements. We demonstrate the application of this method on a multistable reaction-diffusion equation which has four asymptotically stable steady states. Classifications based on SPML predict the asymptotic behavior of initial conditions, based on two-point measurements, with over 89% accuracy. The learned optimal metric also determines where these measurements need to be made to ensure accurate predictions.