19 citations · 26 across the 8 of their papers we have counts for
9 papers
Exact coherent states underlying chaotic falling-film dynamics
Isaac J. G. Lewis, C. Ricardo Constante-Amores
Dynamical-systems approaches to spatiotemporal chaos have been developed primarily for single-phase flows, where the system state is defined by bulk velocity fields. Extending thes…
Stabilizing Rayleigh-Benard convection with reinforcement learning trained on a reduced-order model
Qiwei Chen, C. Ricardo Constante-Amores
Rayleigh-Benard convection (RBC) is a canonical system for buoyancy-driven turbulence and heat transport, central to geophysical and industrial flows. Developing efficient control…
Data-driven modeling of a settling sphere in a quiescent medium
Haoyu Wang, Isaac J. G. Lewis, Soohyeon Kang +3
We develop data-driven models to predict the dynamics of a freely settling sphere in a quiescent Newtonian fluid using experimentally obtained trajectories. Particle tracking veloc…
Low-dimensional multiscale dynamics of intermittent reversals in turbulent Rayleigh-Benard convection
Qiwei Chen, C. Ricardo Constante-Amores
We investigate whether a strongly turbulent flow with intermittent large-scale reorganizations admits a compact state-space description. As a representative high-dimensional chaoti…
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions
Jake Buzhardt, C. Ricardo Constante-Amores, Michael D. Graham
This work explores the relationship between state space methods and Koopman operator-based methods for predicting the time-evolution of nonlinear dynamical systems. We demonstrate…
Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
Complex chaotic dynamics, seen in natural and industrial systems like turbulent flows and weather patterns, often span vast spatial domains with interactions across scales. Accurat…
Dynamics of a Data-Driven Low-Dimensional Model of Turbulent Minimal Pipe Flow
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant times and length scales. However, due to the dissipative nature of the Navier-Stokes e…
Data-driven state-space and Koopman operator models of coherent state dynamics on invariant manifolds
C. Ricardo Constante-Amores, Michael D. Graham
The accurate simulation of complex dynamics in fluid flows demands a substantial number of degrees of freedom, i.e. a high-dimensional state space. Nevertheless, the swift attenuat…
Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
Data-driven approximations of the Koopman operator are promising for predicting the time evolution of systems characterized by complex dynamics. Among these methods, the approach k…