Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery

TL;DR

This paper introduces a method for quickly updating dynamical system models after abrupt changes using limited data, leveraging sparse identification techniques to improve efficiency and robustness.

Contribution

The paper proposes a novel framework combining change detection with sparse model updates to efficiently recover system models with minimal data and modifications.

Findings

Sparse updates outperform full re-identification in data efficiency.

The method is robust to noise and reduces computational complexity.

Effective in both periodic and chaotic systems.

Abstract

Big data has become a critically enabling component of emerging mathematical methods aimed at the automated discovery of dynamical systems, where first principles modeling may be intractable. However, in many engineering systems, abrupt changes must be rapidly characterized based on limited, incomplete, and noisy data. Many leading automated learning techniques rely on unrealistically large data sets and it is unclear how to leverage prior knowledge effectively to re-identify a model after an abrupt change. In this work, we propose a conceptual framework to recover parsimonious models of a system in response to abrupt changes in the low-data limit. First, the abrupt change is detected by comparing the estimated Lyapunov time of the data with the model prediction. Next, we apply the sparse identification of nonlinear dynamics (SINDy) regression to update a previously identified model with the fewest changes, either by addition, deletion, or modification of existing model terms. We demonstrate this sparse model recovery on several examples for abrupt system change detection in periodic and chaotic dynamical systems. Our examples show that sparse updates to a previously identified model perform better with less data, have lower runtime complexity, and are less sensitive to noise than identifying an entirely new model. The proposed abrupt-SINDy architecture provides a new paradigm for the rapid and efficient recovery of a system model after abrupt changes.