27 papers · ranked by Valyu relevance
Mihailo R. Jovanović, Peter J. Schmid, Joseph W. Nichols
Dynamic mode decomposition (DMD) represents an effective means for capturing the essential features of numerically or experimentally generated flow fields. In order to achieve a desirable tradeoff between the quality of approximation and the number of modes that are used to approximate the given fields, we develop a…
Mohd Y. Ali, Anshuman Pandey, James W. Gregory, Vittorio M.N. Passaro
'Vittorio M.N. Passaro'] Fast-response pressure sensitive paint (PSP) is used in this work to measure and analyze the acoustic pressure field in a rectangular cavity. The high spatial resolution and fast frequency response of PSP effectively captures the spatial and temporal detail of surface pressure resulting in the…
James M. Kunert-Graf, Kristian M. Eschenburg, David J. Galas, J. Nathan Kutz + 2 more
Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan…
N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz
We introduce the method of compressed dynamic mode decomposition (cDMD) for background modeling. The dynamic mode decomposition (DMD) is a regression technique that integrates two of the leading data analysis methods in use today: Fourier transforms and singular value decomposition. Borrowing ideas from compressed…
Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
Identifying governing equations from data is a critical step in the modeling and control of complex dynamical systems. Here, we investigate the data-driven identification of nonlinear dynamical systems with inputs and forcing using regression methods, including sparse regression. Specifically, we generalize the sparse…
Jacob Kang, Hunseok Kang, Jong-Hyeon Seo
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are major neurodegenerative disorders with characteristic EEG alterations. While most prior studies have focused on eyes-closed (EC) EEG, where stable alpha rhythms support relatively high classification performance, eyes-open (EO) EEG has proven particularly…
James M. Kunert, Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz + 1 more
'J. Nathan Kutz' 'Saad Jbabdi'] Using a computational model of the Caenorhabditis elegans connectome dynamics, we show that proprioceptive feedback is necessary for sustained dynamic responses to external input. This is consistent with the lack of biophysical evidence for a central pattern generator, and recent…
Miha Rot, Martin Horvat, Gregor Kosec
—The time-dependent fields obtained by solving partial differential equations in two and more dimensions quickly overwhelm the analytical capabilities of the human brain. A meaningful insight into the temporal behaviour can be obtained by using scalar reductions, which, however, come with a loss of spatial detail.…
Yaning Wang, Yihong Wang, Xuying Xu, Xiaochuan Pan
Extracting features from abnormal brain regions in schizophrenia patients’ brain images holds significant importance for aiding diagnosis. However, existing methods remained limited in simultaneously capturing spatiotemporal information. Dynamic mode decomposition (DMD) effectively extracts spatiotemporal features from…
Taku Nonomura, Hisaichi Shibata, Ryoji Takaki, Baogui Xin
A new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm which is an online algorithm for dataset for a…
Ziyou Wu, Steven L. Brunton, Shai Revzen
| 0 | | | Contents | | --- | --- | --- | --- | | 1 | Abstract | | 1 | | 2 | Introduction | | 2 | | 3 | Background | | 2 | | | 3.1 | Dynamic mode decomposition (DMD) | 3 | | | 3.2 Koopman theory | | 4 | | 4 | Methods | | 5 | | | 4.1 | A "simple" linear system | 5 | | | 4.2 | Nonlinear observations of the system | 5 | |…
Santosh Tirunagari, Norman Poh, Kevin Wells, Miroslaw Bober + 2 more
'Isky Gorden' 'David Windridge'] Images of the kidneys using dynamic contrast-enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in…
Jingwei Yin, Bing Liu, Guangping Zhu, Zhinan Xie
It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem. The multiframe data containing the target echo and reverberation are arranged in a matrix, and then, the detection is…
Daniel Dylewsky, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Delay embeddings of time series data have emerged as a promising coordinate basis for data-driven estimation of the Koopman operator, which seeks a linear representation for observed nonlinear dynamics. Recent work has demonstrated the efficacy of Dynamic Mode Decomposition (DMD) for obtaining finite-dimensional…
Jeremy Casorso, Xiaolu Kong, Wang Chi, Dimitri Van De Ville + 2 more
Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain networks shaping functional connectivity (FC). Importantly, these…
Andreas Tuor, Nico Canzani, Tobias Rüggeberg, Stefan Gorenflo + 3 more
'Gerd Simons' 'Bruno Bättig' 'Daniel Iseli'] In many mechanical, electrical, and general physical systems evolving over time or space, spectral analysis methods as Fast Fourier Transform (FFT), Short Term Fourier Transform (STFT), Power Spectrum Density (PSD) plays a very important role. They allow an extraction of…
Minho Song, Oleg A. Sapozhnikov, Vera A. Khokhlova, Tatiana D. Khokhlova
Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse de novo inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining…
Liangliang Cheng, Justin de Groot, Kun Xie, Yanxin Si + 2 more
'Barry K. Lavine'] Accelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations. With rapid advancements in computer vision and deep learning, research into tracking the…
John Ferré, Ariel Rokem, Elizabeth A. Buffalo, J. Nathan Kutz + 1 more
Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this…
N. Benjamin Erichson, Carl Donovan
This paper introduces a fast algorithm for randomized computation of a low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this matrix to represent the development of a spatial grid through time e.g. data from a static video source. DMD was originally introduced in the fluid mechanics community, but…
Miklas P. W. Larsen, Line Lauritsen, Rasmus Jensen, Ralf Zimmermann + 1 more
Ca^2+^ signaling in astrocytes is a central mechanism of intercellular communication in the brain and plays a key role in regulating neuronal excitability, synaptic plasticity, and energy metabolism. Disruption of astrocytic Ca^2+^ dynamics is a characteristic of neurodegenerative diseases, as are deviations in…
Daniel Wüstner
Image segmentation in fluorescence microscopy is often based on spectral separation of fluorescent probes (color-based segmentation) or on significant intensity differences in individual image regions (intensity-based segmentation). These approaches fail, if dye fluorescence shows large spectral overlap with other…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Nicolai Machholdt Høyer, Ove Christiansen
We present a new quasi-direct quantum molecular dynamics computational method which offer a compromise between quantum dynamics using a pre-computed potential energy surface (PES) and fully direct quantum dynamics. This method is termed the time-dependent adaptive density-guided approach (TD-ADGA) and is a method for…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Authors not listed
Vibrational spectroscopy provides a bond-specific view of molecular structure and dynamics, but translating spectroscopic observables to local environments requires the development of accurate models to compute spectroscopic observables from simulations. Recent advances in machine learning interatomic potentials…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…