Article
Computer Science, Software Engineering
Duong B. Nguyen, Panruo Wu, Rodolfo Ostilla Monico, Guoning Chen
Summary: Large-scale structures in shear flows play a significant role in understanding physical phenomena and modeling complex turbulence flows. To address the limitations of conventional methods, we propose the use of Multi-Resolution Dynamic Mode Decomposition (mrDMD) to extract large-scale structures in shear flows. Our method utilizes slow motion DMD modes to capture the dynamics of these structures and provides an efficient way to visualize them. We also provide a GPU-based implementation to speed up the computation of mrDMD.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Chemistry, Multidisciplinary
Yuwei Cheng, Qian Chen
Summary: The study found that the dominant-mode selection criterion based on mode amplitude is more suitable for turbulent mixing layer flow, while the standard dynamic mode decomposition method can accurately reconstruct and predict the region before instability happens.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Kangning Wang, Hongwei Wang, Shaomin Li
Summary: The paper proposes a novel online renewable quantile regression strategy that updates the resulting estimator with current data and summary statistics of historical data, addressing the challenge of implementing quantile regression in a streaming data environment. The new method is computationally efficient, not storage-intensive, and performs well in numerical experiments.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Aleksandr Katrutsa, Sergey Utyuzhnikov, Ivan Oseledets
Summary: The Dynamic Mode Decomposition is an efficient technique for studying dynamic data, but its application becomes problematic when the data is incomplete. To account for the effect of unresolved variables, an optimal prediction approach based on the Mori-Zwanzig formalism can be applied to obtain a time-predictive model that considers the impact of missing data.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Mechanics
Zheming Tong, Jiage Xin, Jiaying Song, Xiangkun Elvis Cao
Summary: In this study, a GPU-accelerated deep neural network-based flow field prediction method is proposed, which achieves good experimental results in turbomachinery and shows better capability in capturing vortex structure details.
Article
Radiology, Nuclear Medicine & Medical Imaging
Efe Ilicak, Safa Ozdemir, Jascha Zapp, Lothar R. Schad, Frank G. Zoellner
Summary: Introduces dynamic mode decomposition (DMD) as a reliable alternative for assessing pulmonary functional information from non-contrast-enhanced dynamic acquisitions. Pulmonary fractional ventilation and normalized perfusion maps were obtained using DMD from simulated phantoms and in vivo dynamic acquisitions of healthy volunteers. The performance of DMD was compared with conventional Fourier decomposition (FD) and matrix pencil (MP) methods in estimating functional map values, and it was found that DMD is capable of successfully obtaining pulmonary functional maps.
MAGNETIC RESONANCE IN MEDICINE
(2023)
Article
Mechanics
Cruz Y. Li, Zengshun Chen, Tim K. T. Tse, Asiri Umenga Weerasuriya, Xuelin Zhang, Yunfei Fu, Xisheng Lin
Summary: This study extends the investigation on the sampling nuances of dynamic mode decomposition (DMD) under Koopman analysis. It confirms the generality of the convergence states for all DMD implementations and reveals the effects of sampling range and resolution on spectral discretization. The study also suggests that observables derived from the same flow may contain dynamically distinct information and provides recommendations for characterizing the structure and flow field using surface pressure and vortex fields.
Article
Mathematics, Applied
Joel A. Rosenfeld, Rushikesh Kamalapurkar
Summary: This manuscript aims to address the limitations of dynamic mode decomposition in the application of Koopman analysis. It proposes modifications and presents viable reconstruction algorithms and convergent DMD algorithms.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2023)
Article
Mathematics, Applied
Travis Askham, Peng Zheng, Aleksandr Aravkin, J. Nathan Kutz
Summary: Dynamic Mode Decomposition (DMD) is a dimensionality reduction algorithm that decomposes time-series data into time dynamics and spatial structures. However, standard Frobenius norm misfit penalty may create biases when data contains outliers or features not well represented by exponentials. To address this, we propose a robust statistical framework and variable projection algorithms, as well as a scalable algorithm combining the variable projection framework with stochastic variance reduction.
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2022)
Article
Computer Science, Information Systems
Guilherme Andrade, Willian Barreiros Jr, Leonardo Rocha, Renato Ferreira, George Teodoro
Summary: This paper presents a distributed memory parallelization solution for similarity search in online content-based multimedia retrieval applications. The solution utilizes the efficient Inverted File System with Asymmetric Distance Computation algorithm and introduces the Multi-Stream Adaptation algorithm for reducing response times. Experimental results demonstrate the solution's high scalability and superior performance.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Economics
Matteo Barigozzi, Matteo Luciani
Summary: This article proposes a new measure of the output gap based on a dynamic factor model estimated on a large number of U.S. macroeconomic indicators, incorporating relevant stylized facts about macroeconomic data. The findings show that the U.S. economy operated above its potential from the mid-1990s to 2008, and in 2018:Q4, the labor market was tighter than the market for goods and services. As a data-driven measure, it serves as a natural complementary tool to theoretical models used at policy institutions.
REVIEW OF ECONOMICS AND STATISTICS
(2023)
Article
Mathematics, Applied
Kensuke Aishima
Summary: This paper focuses on the statistical consistency analysis of the total least squares DMD (TLS-DMD) method, which is known for its robustness in handling random noise in time series data. By providing a statistical model and a general framework for designing projection methods based on efficient dimensionality reduction, the strong consistency of the estimation is proven.
JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS
(2023)
Article
Energy & Fuels
Michael Felix Pacevicius, Marilia Ramos, Davide Roverso, Christian Thun Eriksen, Nicola Paltrinieri
Summary: This paper proposes a method for selecting the best information for urban power-grid risk analysis based on existing risk assessment standards. It introduces a method for reinforcing data-related risk analysis steps and develops a three-phases method for selecting the best datasets. The method is applied to a case study of vegetation-related risk analysis in power grids, demonstrating its capability for dynamic risk analysis in real-case scenarios.
Article
Computer Science, Interdisciplinary Applications
Quincy A. Huhn, Mauricio E. Tano, Jean C. Ragusa, Youngsoo Choi
Summary: Dynamic Mode Decomposition (DMD) is a model-order reduction technique that extracts spatial modes of fixed temporal frequencies from numerical or experimental data. This paper presents two novel approaches to parametric DMD: one based on interpolation of the reduced-order DMD eigen-pair and the other based on interpolation of the reduced DMD (Koopman) operator. Numerical results are provided for diffusion-dominated nonlinear dynamical problems, including a multiphysics radiative transfer example. The three parametric DMD approaches are compared.
JOURNAL OF COMPUTATIONAL PHYSICS
(2023)
Article
Multidisciplinary Sciences
A. B. Albidah, W. Brevis, V. Fedun, I. Ballai, D. B. Jess, M. Stangalini, J. Higham, G. Verth
Summary: The study demonstrates the successful identification of sausage and kink modes in a sunspot umbra using a combination of POD and DMD techniques, providing new insights into solar observations.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Correction
Engineering, Aerospace
Kunihiko Taira, Steven L. Brunton, Scott T. M. Dawson, Clarence W. Rowley, Tim Colonius, Beverley J. McKeon, Oliver T. Schmidt, Stanislav Gordeyev, Vassilios Theofilis, Lawrence S. Ukeiley
Article
Mechanics
Daniel Floryan, Clarence W. Rowley
JOURNAL OF FLUID MECHANICS
(2020)
Article
Biophysics
Sayantan Dutta, Nareg J- Djabrayan, Celia M. Smits, Clarence W. Rowley, Stanislav Y. Shvartsman
BIOPHYSICAL JOURNAL
(2020)
Article
Automation & Control Systems
Leonid Pogorelyuk, Clarence W. Rowley, N. Jeremy Kasdin
Article
Mechanics
Alberto Padovan, Samuel E. Otto, Clarence W. Rowley
JOURNAL OF FLUID MECHANICS
(2020)
Article
Mechanics
Eric A. Deem, Louis N. Cattafesta, Maziar S. Hemati, Hao Zhang, Clarence Rowley, Rajat Mittal
JOURNAL OF FLUID MECHANICS
(2020)
Article
Mathematics
Charles Fefferman, Bernat Guillen Pegueroles, Clarence W. Rowley, Melanie Weber
Summary: This paper presents optimal control strategies for a simple toy problem, where the underlying dynamics depend on an initially unknown and learnable parameter. Different versions of the problem, including Bayesian control and agnostic control, are studied, and strategies minimizing regret are obtained by comparing performance with an opponent who knows the parameter value.
REVISTA MATEMATICA IBEROAMERICANA
(2022)
Article
Mathematics, Applied
Samuel E. Otto, Clarence W. Rowley
Summary: Sensor placement and feature selection are crucial steps in engineering, modeling, and data science. Standard techniques often fail in nonlinear systems, and we propose a new data-driven approach to address this issue.
JOURNAL OF NONLINEAR SCIENCE
(2022)
Article
Mathematics, Applied
Samuel E. Otto, Alberto Padovan, Clarence W. Rowley
Summary: Reduced-order modeling techniques accurately capture dynamics, but neglect low-energy features with high dynamical significance for nonlinear systems far from equilibria. To improve accuracy, we propose optimizing reduced-order models using coarsely sampled trajectories from the original system.
SIAM JOURNAL ON SCIENTIFIC COMPUTING
(2022)
Article
Physics, Fluids & Plasmas
Alberto Padovan, Clarence W. Rowley
Summary: This paper extends the harmonic resolvent analysis to study the dynamics of subharmonic perturbations in periodically time-varying base flows. It recovers an input-output operator related to the harmonic transfer function and elucidates the cross-frequency interactions between subharmonic flow structures. The study shows the importance of these interactions and their impact on the sensitivity of the jet to subharmonic perturbations.
PHYSICAL REVIEW FLUIDS
(2022)
Article
Physics, Fluids & Plasmas
Wen Wu, Charles Meneveau, Rajat Mittal, Alberto Padovan, Clarence W. Rowley, Louis Cattafesta
Summary: The response of a turbulent separation bubble to zero-net-mass-flux actuation is investigated via direct numerical simulations. The results demonstrate that the length of the separation bubble can be reduced by forming vortex pairs at the appropriate excitation frequencies. In addition, the time-averaged structures exhibit a high sensitivity to the actuation.
PHYSICAL REVIEW FLUIDS
(2022)
Article
Mathematics
Jacob Carruth, Maximilian F. Eggl, Charles Fefferman, Clarence W. Rowley, Melanie Weber
Summary: This article presents a simple control problem in which the dynamics depend on an unknown parameter, and introduces an optimal control strategy with an error within a multiplicative constant. Unlike most authors, our strategy achieves the lowest expected cost within a fixed time horizon.
REVISTA MATEMATICA IBEROAMERICANA
(2022)
Article
Mathematics, Applied
Sebastian Peitz, Samuel E. Otto, Clarence W. Rowley
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2020)
Article
Mathematics, Applied
Hao Zhang, Scott T. M. Dawson, Clarence W. Rowley, Eric A. Deem, Louis N. Cattafesta
JOURNAL OF COMPUTATIONAL DYNAMICS
(2020)
Article
Mathematics, Applied
Hao Zhang, Clarence W. Rowley, Eric A. Deem, Louis N. Cattafesta
SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS
(2019)