166 citations · 181 across the 4 of their papers we have counts for
4 papers
Quantum reinforcement learning-based active flow control
Hongfu Zhang, Hui Tang
Active flow control remains a significant challenge due to the high-dimensional, nonlinear nature of fluid dynamics. Quantum machine learning may prove effective in addressing thes…
Machine learning guided discovery of superconducting calcium borocarbides
Chao Zhang, Hui Tang, Chen Pan +4
Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method…
A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation
Hui Tang, Kui Jia
Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. However, exhaustive data annotation is impracticable for each task…
Applying deep reinforcement learning to active flow control in turbulent conditions
Feng Ren, Jean Rabault, Hui Tang
Machine learning has recently become a promising technique in fluid mechanics, especially for active flow control (AFC) applications. A recent work [J. Fluid Mech. (2019), vol. 865…