2 papers
math.DS2021
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…
math.DS2019
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…