Results 171 to 180 of about 40,631 (197)

Wavelet‐based dynamic mode decomposition

PAMM, 2021
AbstractDynamic mode decomposition (DMD) has emerged as a leading data‐driven technique to identify the spatio‐temporal coherent structure in dynamical systems, owing to its strong relation with the Koopman operator. For dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function but should ...
Manu Krishnan, Serkan Gugercin, Pablo Tarazaga   +2 more
openaire   +1 more source

Symbolic extended dynamic mode decomposition

Chaos: An Interdisciplinary Journal of Nonlinear Science
In this paper, we present a new method of performing extended dynamic mode decomposition (EDMD) on systems, which admit a symbolic representation. EDMD generates estimates of the Koopman operator, K, for a dynamical system by defining a dictionary of observables on the space and producing an estimate, Km, which is restricted to be invariant on the span
Connor Kennedy, John Kaushagen, Hong-Kun Zhang   +2 more
openaire   +3 more sources

Analysis of Dynamic Stall using Dynamic Mode Decomposition Technique

31st AIAA Applied Aerodynamics Conference, 2013
Dynamic mode decomposition is applied to investigate the unsteady flowfield around a pitching airfoil. The extracted flow structures are termed as dynamic mode decomposition modes. Analyses are performed for both attached flow and dynamic stall cases. Initially, flowfield data generated from numerical simulations are investigated.
Mariappan, Sathesh, Gardner, Anthony Donald, Richter, Kai, Raffel, Markus   +3 more
openaire   +2 more sources

Sparse nonnegative dynamic mode decomposition

2017 IEEE International Conference on Image Processing (ICIP), 2017
Dynamic mode decomposition (DMD) is a method to extract coherent modes from nonlinear dynamical systems. In this paper, we propose an extension of DMD, sparse nonnegative DMD, which generates a nonlinear and sparse modal representation of dynamics. In particular, this makes DMD more suitable for video processing.
Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi   +2 more
openaire   +1 more source

A Characteristic Dynamic Mode Decomposition

2016
Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models and define either spatial structures in time or temporal structures in space.
Sesterhenn, J��rn, Shahirpour, Amir
openaire   +1 more source

Convergent Dynamic Mode Decomposition

2023 62nd IEEE Conference on Decision and Control (CDC), 2023
Joel A. Rosenfeld, Rushikesh Kamalapurkar   +1 more
openaire   +1 more source

Recent progress and perspectives in heterogeneous photocatalytic CO2 reduction through a solid–gas mode

Coordination Chemistry Reviews, 2021
Hai-Ning Wang, Yifa Chen, Ya-Qian Lan
exaly  

Whispering-gallery-mode sensors for biological and physical sensing

Nature Reviews Methods Primers, 2021
Deshui Yu, Matjaž Humar, Krista Meserve
exaly  

Simultaneously Enhancing Power Density and Durability of Sliding‐Mode Triboelectric Nanogenerator via Interface Liquid Lubrication

Advanced Energy Materials, 2020
Linglin Zhou, Di Liu, Zhi-Hao Zhao
exaly  

An Ultrarobust and High‐Performance Rotational Hydrodynamic Triboelectric Nanogenerator Enabled by Automatic Mode Switching and Charge Excitation

Advanced Materials, 2022
Wencong He, Zhao Wang, Wenlin Liu
exaly