Jianhua Pang
Papers
3
Total Citations
29
H-Index
3
About
Jianhua Pang is a leading researcher at the intersection of computational fluid dynamics, bio-inspired robotics, and artificial intelligence. His work centers on unraveling the complex hydrodynamics of fish locomotion and applying deep reinforcement learning (DRL) to create intelligent, autonomous underwater vehicles. Pang’s major contributions include pioneering the use of DRL for point-to-point navigation of fish-like swimmers in time-varying vortical flows, a breakthrough that directly addresses the challenge of efficient robotic navigation in chaotic real-world environments. His research on target-directed swimming for three-link bionic fish has established robust feedback control systems, achieving precise maneuvering without traditional programming. By simulating the hydrodynamic interactions within fish schools, Pang has also provided critical insights into how motion parameters affect collective behavior and energy efficiency. With his most-cited paper garnering 15 citations since 2022, his work is rapidly gaining recognition. Pang’s innovative hybrid methods—merging immersed boundary-lattice Boltzmann simulations with DRL—are setting new standards for the design of agile, adaptive underwater robots, promising transformative impacts on marine exploration and environmental monitoring.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3