3 papers
physics.flu-dyn2024
Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows
C. Ricardo Constante-Amores, Andrew J. Fox, Carlos E. Pérez De Jesús +1
The Koopman operator enables the analysis of nonlinear dynamical systems through a linear perspective by describing time evolution in the infinite-dimensional space of observables.…
physics.flu-dyn2024
Data-driven low-dimensional model of a sedimenting flexible fiber
Andrew J Fox, Michael D. Graham
The dynamics of flexible filaments entrained in flow, important for understanding many biological and industrial processes, are computationally expensive to model with full-physics…
cs.LG2023
Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems
Kevin Zeng, Carlos E. Pérez De Jesús, Andrew J. Fox +1
While many phenomena in physics and engineering are formally high-dimensional, their long-time dynamics often live on a lower-dimensional manifold. The present work introduces an a…