This paper discusses the spectral proper orthogonal decomposition and its use in identifying
modes, or structures, in flow data. A specific algorithm based on estimating the cross …

We consider the frequency domain form of proper orthogonal decomposition (POD), called
spectral proper orthogonal decomposition (SPOD). Spectral POD is derived from a space …

Data-driven decompositions are becoming essential tools in fluid dynamics, allowing for
tracking the evolution of coherent patterns in large datasets, and for constructing low-order …

We propose a new technique for obtaining reduced order models for nonlinear dynamical
systems. Specifically, we advocate the use of the recently developed dynamic mode …

Physical human–robot interaction tasks require robots that can detect and react to external
perturbations caused by the human partner. In this contribution, we present a machine …

We propose an improved framework for dynamic mode decomposition (DMD) of 2‐D flows
for problems originating from meteorology when a large time step acts like a filter in …

Data-driven decompositions of particle image velocimetry (PIV) measurements are widely
used for a variety of purposes, including the detection of coherent features (eg vortical …

Large eddy simulations (LES) are carried out to investigate the flow around a vibrating
cylinder in the subcritical Reynolds number regime at Re= 3900. Three reduced velocities, U …

It is challenging to construct generalized physical models of underwater wave propagation
owing to their complex physics and widely varying environmental parameters and dynamical …

Abstract A novel Dynamic Mode Decomposition (DMD) technique capable of handling non–
uniformly sampled data is proposed. As it is usual in DMD analysis, a linear relationship …