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
3 Pith papers cite this work. Polarity classification is still indexing.
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An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
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
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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%.
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Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
An LLM-based self-evolving agent discovers a traveling-wave controller with body-frame guidance and yaw feedback that generalizes to unseen targets for an underactuated fluid swimmer.
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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.