1 code implementation • 28 Jul 2023 • Samuel E. Otto, Gregory R. Macchio, Clarence W. Rowley
To begin to address these issues, we introduce a parametric class of nonlinear projections described by constrained autoencoder neural networks in which both the manifold and the projection fibers are learned from data.
1 code implementation • 20 Sep 2022 • Samuel E. Otto, Sebastian Peitz, Clarence W. Rowley
Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control.
no code implementations • 28 Jul 2022 • Samuel E. Otto, Alberto Padovan, Clarence W. Rowley
We provide an efficient snapshot-based computational method analogous to balanced proper orthogonal decomposition.
no code implementations • 27 Jan 2021 • Samuel E. Otto, Clarence W. Rowley
In order to remedy these problems, we introduce a novel data-driven approach for sensor placement and feature selection for a general type of nonlinear inverse problem based on the information contained in secant vectors between data points.
no code implementations • 18 May 2019 • Samuel E. Otto, Clarence W. Rowley
Instead, we propose to identify a small collection of the original variables which are capable of uniquely determining all others either locally via immersion or globally via embedding of the underlying manifold.
no code implementations • 4 Dec 2017 • Samuel E. Otto, Clarence W. Rowley
This paper describes a method for learning low-dimensional approximations of nonlinear dynamical systems, based on neural-network approximations of the underlying Koopman operator.
3 code implementations • 5 Feb 2017 • Kunihiko Taira, Steven L. Brunton, Scott T. M. Dawson, Clarence W. Rowley, Tim Colonius, Beverley J. McKeon, Oliver T. Schmidt, Stanislav Gordeyev, Vassilios Theofilis, Lawrence S. Ukeiley
Simple aerodynamic configurations under even modest conditions can exhibit complex flows with a wide range of temporal and spatial features.
Fluid Dynamics