96 citations · 285 across the 13 of their papers we have counts for
6 papers · 1 filter
Extracting dominant dynamics about unsteady base flows
Alec J. Linot, Barbara Lopez-Doriga, Yonghong Zhong +1
A wide range of techniques exist for extracting the dominant flow dynamics and features about steady, or periodic base flows. However, there have been limited efforts in extracting…
Hierarchical equivariant graph neural networks for forecasting collective motion in vortex clusters and microswimmers
Alec J. Linot, Haotian Hang, Eva Kanso +1
Data-driven modeling of collective dynamics is a challenging problem because emergent phenomena in multi-agent systems are often shaped by long-range interactions among individuals…
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…
On the laminar solutions and stability of accelerating and decelerating channel flows
Alec J. Linot, Peter J. Schmid, Kunihiko Taira
We study the effect of acceleration and deceleration on the stability of channel flows. To do so, we derive an exact solution for laminar profiles of channel flows with arbitrary,…
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning
Alec J. Linot, Kevin Zeng, Michael D. Graham
The high dimensionality and complex dynamics of turbulent flows remain an obstacle to the discovery and implementation of control strategies. Deep reinforcement learning (RL) is a…
Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow
Alec J. Linot, Michael D. Graham
Because the Navier-Stokes equations are dissipative, the long-time dynamics of a flow in state space are expected to collapse onto a manifold whose dimension may be much lower than…