21 citations · 51 across the 16 of their papers we have counts for
9 papers · 1 filter
Data-driven discovery of governing equations for coarse-grained heterogeneous network dynamics
Katherine Owens, J. Nathan Kutz
We leverage data-driven model discovery methods to determine the governing equations for the emergent behavior of heterogeneous networked dynamical systems. Specifically, we consid…
Data-driven sensor placement with shallow decoder networks
Jan Williams, Olivia Zahn, J. Nathan Kutz
Sensor placement is an important and ubiquitous problem across the engineering and physical sciences for tasks such as reconstruction, forecasting and control. Surprisingly, there…
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…
Numerical differentiation of noisy data: A unifying multi-objective optimization framework
Floris van Breugel, J. Nathan Kutz, Bingni W. Brunton
Computing derivatives of noisy measurement data is ubiquitous in the physical, engineering, and biological sciences, and it is often a critical step in developing dynamic models or…
Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation
George Stepaniants, Bingni W. Brunton, J. Nathan Kutz
Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given dat…
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…