Deep reinforcement learning discovers high-frequency bang-bang and low-frequency lock-on rotary controls that suppress vibrations in fully and underactuated tandem cylinders by 70-95%.
Triantafyllou, and George Em Karniadakis
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
physics.flu-dyn 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.
citing papers explorer
-
Deep Reinforcement Learning Discovers a Novel Control Algorithm for Mitigating Flow-Induced Vibrations in Underactuated Tandem Cylinders
Deep reinforcement learning discovers high-frequency bang-bang and low-frequency lock-on rotary controls that suppress vibrations in fully and underactuated tandem cylinders by 70-95%.
-
Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.