Yaodong Yang
Papers
2
Total Citations
6
H-Index
2
About
Yaodong Yang is a researcher whose work bridges the frontiers of non-destructive evaluation and artificial intelligence. His primary research areas include advanced thermographic inspection, robotic sensing systems, and model-based reinforcement learning. Yang made a significant contribution to aerospace safety with his pioneering work on robot-assisted rotary linear laser scanning thermography, which dramatically enhanced the detection of closed cracks in solid propellant grain internal surfaces—a critical challenge for propulsion system reliability. This highly innovative paper has already garnered 4 citations since its 2025 publication, signaling its immediate impact. In parallel, Yang has advanced the field of reinforcement learning through his development of differentiable information-enhanced model-based methods. By leveraging the rich gradient information available in differentiable environments, his approach offers a powerful alternative to traditional model-free techniques, enabling more efficient policy learning. This work, published in 2025 with 2 citations, demonstrates his ability to integrate theoretical insights with practical algorithmic improvements. Yang’s dual expertise in hardware-driven inspection and software-based learning positions him as a versatile innovator, with his contributions holding promise for both industrial automation and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Differentiable Information Enhanced Model-Based Reinforcement Learning2 citations · 2025