Fueled by breakthrough technology developments, the biological, biomedical, and
behavioral sciences are now collecting more data than ever before. There is a critical need …

On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

The discovery of governing equations from scientific data has the potential to transform data-
rich fields that lack well-characterized quantitative descriptions. Advances in sparse …

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 …

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 …

Abstract Machine learning is increasingly recognized as a promising technology in the
biological, biomedical, and behavioral sciences. There can be no argument that this …

One way to understand time-series data is to identify the underlying dynamical system which
generates it. This task can be done by selecting an appropriate model and a set of …

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 …

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

This paper presents the use of spike-and-slab (SS) priors for discovering governing
differential equations of motion of nonlinear structural dynamic systems. The problem of …