We develop an algorithm for model selection which allows for the consideration of a
combinatorially large number of candidate models governing a dynamical system. The …

Hybrid systems are traditionally difficult to identify and analyse using classical dynamical
systems theory. Moreover, recently developed model identification methodologies largely …

Model selection and parameter estimation are important for the effective integration of
experimental data, scientific theory, and precise simulations. In this work, we develop a …

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 …

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 …

This paper presents a new Bayesian approach to equation discovery–combined structure
detection and parameter estimation–for system identification (SI) in nonlinear structural …

Sparse model identification by means of data is especially cumbersome if the sought
dynamics live in a high dimensional space. This usually involves the need for large amount …

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 …