504 citations · 690 across the 13 of their papers we have counts for
13 papers
Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss
Peter Yatsyshin, Karl Lapo, Jonathan Smith +4
Antarctic sea ice has undergone unprecedented changes in recent years, raising questions about how this key geophysical system is responding to climate change. Decades of slow expa…
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
Meghana Velegar, Christoph Keller, J. Nathan Kutz
We introduce the optimized dynamic mode decomposition algorithm for constructing an adaptive and computationally efficient reduced order model and forecasting tool for global atmos…
PyDMD: A Python package for robust dynamic mode decomposition
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo +5
The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's l…
Quantifying yeast colony morphologies with feature engineering from time-lapse photography
Andy Goldschmidt, James Kunert-Graf, Adrian C. Scott +3
Baker's yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we co…
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems
Daniel Dylewsky, David Barajas-Solano, Tong Ma +2
Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modele…
Dynamic mode decomposition for multiscale nonlinear physics
Daniel Dylewsky, Molei Tao, J. Nathan Kutz
We present a data-driven method for separating complex, multiscale systems into their constituent time-scale components using a recursive implementation of dynamic mode decompositi…
Data-driven Spatiotemporal Modal Decomposition for Time Frequency Analysis
Seth M. Hirsh, Bingni W. Brunton, J. Nathan Kutz
We propose a new solution to the blind source separation problem that factors mixed time-series signals into a sum of spatiotemporal modes, with the constraint that the temporal co…
Randomized Nonnegative Matrix Factorization
N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn +1
Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental deco…
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals
Lucas Stolerman, Pedro Maia, J. Nathan Kutz
Local climate conditions play a major role in the development of the mosquito population responsible for transmitting Dengue Fever. Since the {\em Aedes Aegypti} mosquito is also a…
Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries
Bethany Lusch, Eric C. Chi, J. Nathan Kutz
We consider -way data arrays and low-rank tensor factorizations where the time mode is coded as a sparse linear combination of temporal elements from an over-complete library. O…
Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
Bingni W. Brunton, Lise A. Johnson, Jeffrey G. Ojemann +1
There is a broad need in the neuroscience community to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes simul…
Selecting a Small Set of Optimal Gestures from an Extensive Lexicon
Jacob Grosek, J. Nathan Kutz
Finding the best set of gestures to use for a given computer recognition problem is an essential part of optimizing the recognition performance while being mindful to those who may…
Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video
Jacob Grosek, J. Nathan Kutz
This paper introduces the method of dynamic mode decomposition (DMD) for robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time…