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 …

Harnessing data to discover the underlying governing laws or equations that describe the
behavior of complex physical systems can significantly advance our modeling, simulation …

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 …

Despite recent achievements in the design and manufacture of advanced materials, the
contributions from first-principles modeling and simulation have remained limited, especially …

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 …

Discovering governing physical laws from noisy data is a grand challenge in many science
and engineering research areas. We present a new approach to data-driven discovery of …

Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of
reduced-order models for a variety of scientific and engineering tasks. However, it is …

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 …

We propose a probabilistic model discovery method for identifying ordinary differential
equations governing the dynamics of observed multivariate data. Our method is based on …