Papers
2
Total Citations
57
H-Index
2
About
Laiping Zhang is a leading researcher at the intersection of computational fluid dynamics and artificial intelligence, specializing in bio-inspired underwater propulsion and autonomous maneuvering. Their pioneering work focuses on developing numerical simulation methods that integrate hydrodynamics, kinematics, and motion control to model self-propelled swimming in bionic fish. Zhang’s major contributions include the first coupling of deep reinforcement learning with the Navier-Stokes equations in an arbitrary Lagrangian-Eulerian framework, enabling fish models to learn and execute complex swimming behaviors autonomously. Their 2020 paper on deep reinforcement learning for self-propelled swimming has garnered 36 citations, while their 2021 study on obstacle avoidance maneuvering has received 21 citations, demonstrating significant impact in the emerging field of intelligent underwater robotics. By using a NACA0012 airfoil as a simplified fish model, Zhang has created a computational testbed for studying how autonomous agents can navigate complex environments—work that has direct implications for the design of next-generation autonomous underwater vehicles capable of adaptive, energy-efficient locomotion.
Research Focus
Key Achievements
Top Papers
- 1
- 2