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20192023
most citedRobust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

216 citations · 242 across the 4 of their papers we have counts for

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physics.flu-dyn2023★ 25 cited

Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections

Hongwei Tang, Yan Wang, Tongguang Wang +1

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom…

physics.flu-dyn2020★ 216 cited

Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

Hongwei Tang, Jean Rabault, Alexander Kuhnle +2

This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely,…