26 citations · 26 across the 7 of their papers we have counts for
9 papers
PRONE: Petrov-Galerkin Operator Learning Unifies DMD, SINDy & Koopmanism
Matthew J. Colbrook, April Herwig, J. Nathan Kutz
Data-driven dynamics often asks how to linearize a nonlinear system. We ask instead: which observables should be advanced, and where should their futures live? This leads to Petrov…
An Interpretable Data-Driven Model of the Flight Dynamics of Hawks
Lydia France, Karl Lapo, J. Nathan Kutz
Despite significant analysis of bird flight, generative physics models for flight dynamics do not currently exist. Yet the underlying mechanisms responsible for various flight mano…
Accelerating scientific discovery with the common task framework
J. Nathan Kutz, Peter Battaglia, Michael Brenner +12
Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical,…
PySensors 2.0: A Python Package for Sparse Sensor Placement
Niharika Karnik, Yash Bhangale, Mohammad G. Abdo +6
PySensors is a Python package for selecting and placing a sparse set of sensors for reconstruction and classification tasks. In this major update to PySensors, we introduce spatial…
CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics
Romulo B. da Silva, Diego Passos, Cássio M. Oishi +1
We present CS-SHRED, a novel deep learning architecture that integrates Compressed Sensing (CS) into a Shallow Recurrent Decoder (SHRED) to reconstruct spatiotemporal dynamics from…
PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
David Ye, Jan Williams, Mars Gao +4
SHallow REcurrent Decoders (SHRED) provide a deep learning strategy for modeling high-dimensional dynamical systems and/or spatiotemporal data from dynamical system snapshot observ…
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…
Data-driven methods to discover stable linear models of the helicity injectors on HIT-SIU
Zachary L. Daniel, Alan A. Kaptanoglu, Christopher J. Hansen +3
Accurate and efficient circuit models are necessary to control the power electronic circuits found on plasma physics experiments. Tuning and controlling the behavior of these circu…
Multi-Resolution Dynamic Mode Decomposition
J. Nathan Kutz, Xing Fu, Steven L. Brunton
We demonstrate that the integration of the recently developed dynamic mode decomposition (DMD) with a multi-resolution analysis allows for a decomposition method capable of robustl…