Weibo Liu
Papers
4
Total Citations
152
H-Index
3
About
Weibo Liu is a leading researcher at the intersection of computational intelligence and advanced manufacturing, with key contributions in swarm intelligence, digital twinning, and physics-informed machine learning. His highly cited survey on particle swarm optimization (PSO)—garnering 107 citations—provides a comprehensive taxonomy of algorithms, applications, and emerging trends, establishing a foundational reference for researchers in heuristic optimization. Liu has pioneered the integration of digital twin technology with robotic process automation, demonstrating in his 2020 work on greenfield hospitals how virtual replicas can unlock significant productivity gains. More recently, he has advanced robotic machining through novel domain-adaptation-assisted dual-task learning, enabling accurate coprediction of efficiency and quality across varying parameter spaces. His 2025 paper on closed-loop parameter optimization further pushes boundaries by combining physics-informed machine learning with multiobjective optimization to overcome the bottleneck of time-consuming experimental tuning. With a growing citation impact and a trajectory toward intelligent, data-driven manufacturing, Liu’s work is shaping the future of autonomous robotic systems and smart production.
Research Focus
Key Achievements
Top Papers
- 1A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization107 citations · 2023
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