Dengqing Cao

Shandong University of Technology, Harbin Institute of Technology

Papers

Total Citations

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About

Dengqing Cao is a leading researcher in structural dynamics, with a focus on the nonlinear behavior of joint-assembled and origami-inspired systems. Their work bridges computational modeling and experimental validation, particularly through the innovative application of physics-informed neural networks (PINNs) for joint identification and low-dimensional dynamical modeling of complex structures. This approach, detailed in their 2025 paper (6 citations), offers a transformative method for capturing the nonlinearities inherent in bolted joints, enabling more accurate and efficient simulations. Cao also explores the dynamics of flexible multi-stable origami structures with bio-inspired creases (2025, 5 citations), advancing the understanding of deployable and adaptive systems. Their contributions are vital for aerospace, robotics, and mechanical design, where precise dynamic behavior is critical. With a growing citation record and a focus on integrating machine learning with classical mechanics, Cao is shaping the future of smart, data-driven structural analysis. Their work exemplifies how modern computational tools can unlock new insights into complex mechanical systems.