Abstract Machine learning methods have revolutionized studies in several areas of
knowledge, helping to understand and extract information from experimental data. Recently …

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 …

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 …

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 …

Dynamical systems play a fundamental role related understanding phenomena inherent to
several fields of science. Technological advances over the previous several decades have …

Abstract Sparse Identification of Nonlinear Dynamics (SINDy) has been shown to
successfully recover governing equations from data; however, this approach assumes the …

Modern societies have an abundance of data yet good system models are rare.
Unfortunately, many of the current system identification and machine learning techniques fail …

Sparse model identification enables the discovery of nonlinear dynamical systems purely
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …

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