35 citations · 47 across the 4 of their papers we have counts for
5 papers
Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures
Sara M. Ichinaga, Steven L. Brunton, Aleksandr Y. Aravkin +1
The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new varia…
Unsupervised multi-scale diagnostics
Karl Lapo, Sara M. Ichinaga, Nathan Kutz
The unsupervised and principled diagnosis of multi-scale data is a fundamental obstacle in modern scientific problems from, for instance, weather and climate prediction, neurology,…
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…
Network inference via process motifs for lagged correlation in linear stochastic processes
Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton
A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in a…
Structured Time-Delay Models for Dynamical Systems with Connections to Frenet-Serret Frame
Seth M. Hirsh, Sara M. Ichinaga, Steven L. Brunton +2
Time-delay embeddings and dimensionality reduction are powerful techniques for discovering effective coordinate systems to represent the dynamics of physical systems. Recently, it…