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 …

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 …

Numerous researches have shown that there are three main challenges in data-driven
model identification methods: high-dimensional measurements, system complexity and …

The ability to discover physical laws and governing equations from data is one of
humankind's greatest intellectual achievements. A quantitative understanding of dynamic …

Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling
and control efforts, providing a tremendous opportunity to extend the reach of model …

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 …

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

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

The sparse identification of nonlinear dynamics (SINDy) has been established as an
effective technique to produce interpretable models of dynamical systems from time …