Policy-DRIFT combines conditional flow matching with terminal reward guidance and decoupled DRL to achieve 49% drag reduction in Re_tau=180 channel flow, 16% above DRL benchmarks and with 37 times less actuation energy.
Improving turbulence control through explainable deep learning
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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physics.flu-dyn 3verdicts
UNVERDICTED 3representative citing papers
Zero-shot RL control trained on matched channel flows reduces skin-friction drag 28.7% and total drag 10.7% on a NACA4412 wing, outperforming opposition control.
VIVALDy is a hybrid β-VAE-GAN plus bidirectional transformer framework that reconstructs and predicts turbulent flow around a one-degree-of-freedom moving cylinder using only cylinder displacement as input.
citing papers explorer
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Policy-DRIFT: Dynamic Reward-Informed Flow Trajectory Steering
Policy-DRIFT combines conditional flow matching with terminal reward guidance and decoupled DRL to achieve 49% drag reduction in Re_tau=180 channel flow, 16% above DRL benchmarks and with 37 times less actuation energy.
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Physics-guided surrogate learning enables zero-shot control of turbulent wings
Zero-shot RL control trained on matched channel flows reduces skin-friction drag 28.7% and total drag 10.7% on a NACA4412 wing, outperforming opposition control.
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VIVALDy: A Hybrid Generative Reduced-Order Model for Turbulent Flows, Applied to Vortex-Induced Vibrations
VIVALDy is a hybrid β-VAE-GAN plus bidirectional transformer framework that reconstructs and predicts turbulent flow around a one-degree-of-freedom moving cylinder using only cylinder displacement as input.