The dynamic mode decomposition (DMD) is a powerful data-driven modeling technique that
reveals coherent spatiotemporal patterns from dynamical system snapshot observations …

Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical
time-step** algorithms to approximate solutions. Further, many systems characterized by …

Abstract Dynamic Mode Decomposition (DMD) is a model-order reduction approach,
whereby spatial modes of fixed temporal frequencies are extracted from numerical or …

The unsupervised and principled diagnosis of multiscale data is a fundamental obstacle in
modern scientific problems from, for instance, weather and climate prediction, neurology …

Delay embeddings of time series data have emerged as a promising coordinate basis for
data-driven estimation of the Koopman operator, which seeks a linear representation for …

A dynamic mode decomposition (DMD) based reduced-order model (ROM) is developed for
tracking, detection, and prediction of kinetic plasma behavior. DMD is applied to the high …

The heat dissipation from the battery surface significantly affects battery performance and
lifetime. This study proposes a new and an alternative method to predict the thermal …

We investigate the sensitivity of reduced order models (ROMs) to training data spatial
resolution as well as sampling rate. In particular, we consider proper orthogonal …