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

Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal
patterns from multi-dimensional time series, and it has been used successfully in a wide …

Dynamic mode decomposition has emerged as a leading technique to identify
spatiotemporal coherent structures from high-dimensional data, benefiting from a strong …

In this paper, the sparse sensor placement problem for least-squares estimation is
considered, and the previous novel approach of the sparse sensor selection algorithm is …

We propose a noise reduction method for unsteady pressure-sensitive paint (PSP) data
based on modal expansion, the coefficients of which are determined from time-series data at …

We propose a new method for computing dynamic mode decomposition evolution matrices,
which we use to analyze dynamical systems. Unlike the majority of existing methods, our …

High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is
challenging. Direct blood flow measurement inside the body with in-vivo measurement …

Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to
decompose complex, nonlinear systems into a set of modes, revealing underlying patterns …

Objective functions for sensor selection are investigated in linear time-invariant systems with
a large number of sensor candidates. This study compared the performance of sensor sets …

A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed.
This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in …