1.9k citations · 5.7k across the 37 of their papers we have counts for
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Linearly-Recurrent Autoencoder Networks for Learning Dynamics
Samuel E. Otto, Clarence W. Rowley
This paper describes a method for learning low-dimensional approximations of nonlinear dynamical systems, based on neural-network approximations of the underlying Koopman operator.…
Evaluating the accuracy of the dynamic mode decomposition
Hao Zhang, Scott T. M. Dawson, Clarence W. Rowley +2
Dynamic mode decomposition (DMD) gives a practical means of extracting dynamic information from data, in the form of spatial modes and their associated frequencies and growth/decay…
Online dynamic mode decomposition for time-varying systems
Hao Zhang, Clarence W. Rowley, Eric A. Deem +1
Dynamic mode decomposition (DMD) is a popular technique for modal decomposition, flow analysis, and reduced-order modeling. In situations where a system is time varying, one would…
Scaling the propulsive performance of heaving and pitching foils
Daniel Floryan, Tyler Van Buren, Clarence W. Rowley +1
Scaling laws for the propulsive performance of rigid foils undergoing oscillatory heaving and pitching motions are presented. Water tunnel experiments on a nominally two-dimensiona…
Modal Analysis of Fluid Flows: An Overview
Kunihiko Taira, Steven L. Brunton, Scott T. M. Dawson +7
Simple aerodynamic configurations under even modest conditions can exhibit complex flows with a wide range of temporal and spatial features. It has become common practice in the an…