115 citations · 379 across the 31 of their papers we have counts for
12 papers · 1 filter
Challenges in Dynamic Mode Decomposition
Ziyou Wu, Steven L. Brunton, Shai Revzen
Dynamic Mode Decomposition (DMD) is a powerful tool for extracting spatial and temporal patterns from multi-dimensional time series, and it has been used successfully in a wide ran…
Deep Learning of Conjugate Mappings
Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz
Despite many of the most common chaotic dynamical systems being continuous in time, it is through discrete time mappings that much of the understanding of chaos is formed. Henri Po…
Modern Koopman Theory for Dynamical Systems
Steven L. Brunton, Marko Budišić, Eurika Kaiser +1
The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymp…
Data-Driven Stabilization of Periodic Orbits
Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton
Periodic orbits are among the simplest non-equilibrium solutions to dynamical systems, and they play a significant role in our modern understanding of the rich structures observed…
PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
Brian M. de Silva, Kathleen Champion, Markus Quade +3
PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonli…
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs
Craig Gin, Bethany Lusch, Steven L. Brunton +1
We develop a deep autoencoder architecture that can be used to find a coordinate transformation which turns a nonlinear PDE into a linear PDE. Our architecture is motivated by the…