| A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method Y Zhu, FB Tian, J Young, JC Liao, JCS Lai Scientific Reports 11 (1), 1691, 2021 | 74 | 2021 |
| Stable schooling formations emerge from the combined effect of the active control and passive self-organization Y Zhu, JH Pang, FB Tian Fluids 7 (1), 41, 2022 | 15 | 2022 |
| Towards end-to-end formation control for robotic fish via deep reinforcement learning with non-expert imitation Y Sun, C Yan, X Xiang, H Zhou, D Tang, Y Zhu Ocean Engineering 271, 113811, 2023 | 10 | 2023 |
| Point-to-point navigation of a fish-like swimmer in a vortical flow with deep reinforcement learning Y Zhu, JH Pang, FB Tian Frontiers in Physics 10, 870273, 2022 | 10 | 2022 |
| Learning to school in dense configurations with multi-agent deep reinforcement learning Y Zhu, JH Pang, T Gao, FB Tian Bioinspiration & Biomimetics 18 (1), 015003, 2022 | 9 | 2022 |
| Enhancing efficiency and propulsion in bio-mimetic robotic fish through end-to-end deep reinforcement learning X Cui, B Sun, Y Zhu, N Yang, H Zhang, W Cui, D Fan, J Wang Physics of Fluids 36 (3), 2024 | 7 | 2024 |
| Effects of fluid rheology on dynamics of a capsule through a microchannel constriction J Ma, Q Huang, Y Zhu, YQ Xu, FB Tian Physics of Fluids 35 (9), 2023 | 7 | 2023 |
| A numerical simulation of target-directed swimming for a three-link bionic fish with deep reinforcement learning Y Zhu, JH Pang Proceedings of the Institution of Mechanical Engineers, Part C: Journal of …, 2023 | 7 | 2023 |
| 二维鳐鱼模型的近壁面增推效应 朱毅， 余永亮 中国科学院大学学报, 2017 | 1 | 2017 |
| 波动翼产生推力的涡动力学分析 余永亮， 朱毅 第九届全国流体力学学术会议论文摘要集, 2016 | | 2016 |
Fang-Bao TianAssociate Professor, University of New South WalesAdresse e-mail validée de unsw.edu.au
