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20222025
most citedEffective control of two-dimensional Rayleigh--Bénard convection: invariant multi-agent reinforcement learning is all you need

57 citations · 108 across the 17 of their papers we have counts for

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Showing 2024 · physics.flu-dynShow all

6 papers · 2 filters

physics.flu-dyn2024★ 2 cited

On Deep-Learning-Based Closures for Algebraic Surrogate Models of Turbulent Flows

Benet Eiximeno, Marcial Sanchís-Agudo, Arnau Miró +3

A deep-learning-based closure model to address energy loss in low-dimensional surrogate models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a transform…

physics.flu-dyn2024★ 7 cited

Deep reinforcement learning for the management of the wall regeneration cycle in wall-bounded turbulent flows

Giorgio Maria Cavallazzi, Luca Guastoni, Ricardo Vinuesa +1

The wall cycle in wall-bounded turbulent flows is a complex turbulence regeneration mechanism that remains not fully understood. This study explores the potential of deep reinforce…

physics.flu-dyn2024

Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convection

Joel Vasanth, Jean Rabault, Francisco Alcántara-Ávila +2

Deep reinforcement learning (DRL) has found application in numerous use-cases pertaining to flow control. Multi-agent RL (MARL), a variant of DRL, has shown to be more effective th…

physics.flu-dyn2024★ 5 cited

Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at

P. Suárez, F. Álcantara-Ávila, A. Miró +4

This study presents novel drag reduction active-flow-control (AFC) strategies} for a three-dimensional cylinder immersed in a flow at a Reynolds number based on freestream velocity…

physics.flu-dyn2024

Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning

P. Suárez, F. Alcántara-Ávila, J. Rabault +4

Designing active-flow-control (AFC) strategies for three-dimensional (3D) bluff bodies is a challenging task with critical industrial implications. In this study we explore the pot…

physics.flu-dyn2024★ 13 cited

Active flow control of a turbulent separation bubble through deep reinforcement learning

Bernat Font, Francisco Alcántara-Ávila, Jean Rabault +2

The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at on the upstream region be…