Identifying governing equations from data is a critical step in the modeling and control of
complex dynamical systems. Here, we investigate the data-driven identification of nonlinear …

Automated data-driven modeling, the process of directly discovering the governing
equations of a system from data, is increasingly being used across the scientific community …

Accurately modelling the nonlinear dynamics of a system from measurement data is a
challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm …

This work proposes an iterative sparse-regularized regression method to recover governing
equations of nonlinear dynamical systems from noisy state measurements. The method is …

A major challenge in the study of dynamical systems is that of model discovery: turning data
into models that are not just predictive, but provide insight into the nature of the underlying …

PySINDy is a Python package for the discovery of governing dynamical systems models
from data. In particular, PySINDy provides tools for applying the sparse identification of …

Discovering governing equations of complex dynamical systems directly from data is a
central problem in scientific machine learning. In recent years, the sparse identification of …

Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …

Extracting governing equations from data is a central challenge in many diverse areas of
science and engineering. Data are abundant whereas models often remain elusive, as in …

The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the
discovery of parsimonious dynamic models and governing equations from time-series data …