28 citations · 31 across the 4 of their papers we have counts for
4 papers
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…
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…
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning
Kevin Zeng, Alec J. Linot, Michael D. Graham
Deep reinforcement learning (RL) is a data-driven method capable of discovering complex control strategies for high-dimensional systems, making it promising for flow control applic…
Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics
Kevin Zeng, Michael D. Graham
Deep reinforcement learning (RL) is a data-driven, model-free method capable of discovering complex control strategies for macroscopic objectives in high-dimensional systems, makin…