Learning Nonlinear Dynamics Using Kalman Smoothing

TL;DR

This paper integrates Kalman smoothing into the SINDy framework for identifying nonlinear ODEs from noisy data, demonstrating improved robustness and ease of parameter tuning over traditional derivative estimation methods.

Contribution

The authors incorporate Kalman smoothing into SINDy, enhancing ODE discovery from noisy measurements and providing a practical, optimized implementation within pysindy.

Findings

Kalman smoothing outperforms finite difference and filtering methods in noisy conditions.

The integrated method is easier to tune and preserves problem structure.

Numerical experiments confirm improved robustness in ODE identification.

Abstract

Identifying Ordinary Differential Equations (ODEs) from measurement data requires both fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data. The Sparse Identification of Nonlinear Dynamics (SINDy) method involves a derivative estimation step (and optionally, smoothing) and a sparse regression step on a library of candidate ODE terms. Kalman smoothing is a classical framework for assimilating the measurement data with known noise statistics. Previously, derivatives in SINDy and its python package, pysindy, had been estimated by finite difference, L1 total variation minimization, or local filters like Savitzky-Golay. In contrast, Kalman allows discovering ODEs that best recreate the essential dynamics in simulation, even in cases when it does not perform as well at recovering coefficients, as measured by their F1 score and mean absolute error. We have incorporated Kalman smoothing, along with hyperparameter optimization, into the existing pysindy architecture, allowing for rapid adoption of the method. Numerical experiments on a number of dynamical systems show Kalman smoothing to be the most amenable to parameter selection and best at preserving problem structure in the presence of noise.