The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

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 …

Data-driven transformations that reformulate nonlinear systems in a linear framework have
the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics …

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 …

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 …

High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional
subspace. Engineering applications for modeling, characterization, design, and control of …

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 Koopman theory, a concept to globally model nonlinear signals by a linear Hamiltonian,
has been at the frontier of fluid mechanics research for the last decade. Wind engineering …

Convection is a fundamental fluid transport phenomenon, where the large-scale motion of a
fluid is driven, for example, by a thermal gradient or an electric potential. Modelling …