Publications (7)
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…
The impact of process steps on nearly ideal subthreshold slope in 300-mm compatible InGaZnO TFT
Hongwei Tang, Dennis Lin, Subhali Subhechha +14
While we demonstrate a back-gated (BG) amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) transistors with a nearly ideal subthreshold slope (SS) ~ 60 mV/dec. However, SS degrades when a…
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…
TriLoRA: Integrating SVD for Advanced Style Personalization in Text-to-Image Generation
Chengcheng Feng, Mu He, Qiuyu Tian +4
As deep learning technology continues to advance, image generation models, especially models like Stable Diffusion, are finding increasingly widespread application in visual arts c…
Subthreshold Swing Behavior in Amorphous Indium-Gallium-Zinc-Oxide Transistors from Room to Cryogenic Temperatures
Hongwei Tang, Attilio Belmonte, Dennis Lin +10
While cryogenic-temperature subthreshold swing (SS) in crystalline semiconductors has been widely studied, a careful study on the temperature-dependent SS in amorphous oxide semico…
MoS 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…
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,…