35 citations · 35 across the 3 of their papers we have counts for
4 papers
Sparsifying Priors for Bayesian Uncertainty Quantification in Model Discovery
Seth M. Hirsh, David A. Barajas-Solano, J. Nathan Kutz
We propose a probabilistic model discovery method for identifying ordinary differential equations (ODEs) governing the dynamics of observed multivariate data. Our method is based o…
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…
Centering Data Improves the Dynamic Mode Decomposition
Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz +1
Dynamic mode decomposition (DMD) is a data-driven method that models high-dimensional time series as a sum of spatiotemporal modes, where the temporal modes are constrained by line…
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…