Hongwei Tang

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

216 citations · 217 across the 3 of their papers we have counts for

collaborators

3 papers

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,…

physics.app-ph2019

MoS $_{2}$ Dual-gate Transistors with Electrostatically Doped Contacts

Fuyou Liao, Yaocheng Sheng, Zhongxun Guo +15

Two-dimensional (2D) transition metal dichalcogenides (TMDs) such as molybdenum disulfide (MoS2) have been intensively investigated because of their exclusive physical properties f…

physics.app-ph2019★ 1 cited

High-Performance Logic and Memory Devices Based on a Dual-Gated MoS2 Architecture

Fuyou Liao, Zhongxun Guo, Yin Wang +13

In this work, we demonstrate a dual-gated (DG) MoS2 field effect transistors (FETs) in which the degraded switching performance of multilayer MoS2 can be compensated by the DG stru…

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

MoS2​ Dual-gate Transistors with Electrostatically Doped Contacts

High-Performance Logic and Memory Devices Based on a Dual-Gated MoS2 Architecture

MoS $_{2}$ Dual-gate Transistors with Electrostatically Doped Contacts