Papers
2
Total Citations
37
H-Index
2
About
Leilei Chen is a rising researcher at the intersection of underwater robotics, acoustics, and artificial intelligence. Their work focuses on developing advanced computational methods for vibro-acoustic coupling and underwater acoustic simulation, with a particular emphasis on bio-inspired robotic systems like the manta ray. Chen’s major contributions include pioneering the use of deep learning for uncertainty quantification in vibro-acoustic coupling problems, a critical step toward more reliable and efficient underwater vehicle design. Their most-cited paper (2024, 30 citations) introduces a deep learning framework that dramatically reduces computational costs while maintaining high accuracy in predicting acoustic and vibrational responses. More recently, Chen has advanced the field with a NeuS-assisted boundary element approach for generating underwater acoustic simulations from multi-view sonar images (2025, 7 citations), offering a novel pathway for reconstructing complex acoustic fields. This work holds promise for applications in autonomous underwater navigation, marine biology monitoring, and naval defense. Though early in their career, Chen’s integration of physics-based modeling with modern machine learning techniques is already shaping the next generation of underwater robotic systems.
Research Focus
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Top Papers
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