Yilong Wang

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About

Yilong Wang is a rising researcher at the intersection of computational mechanics, structural dynamics, and bio-inspired design. His work focuses on developing data-driven and physics-informed methods to model complex mechanical systems, particularly joint-assembled structures and flexible origami. In his highly cited 2025 paper, "PINN-based joint identification and low-dimensional dynamical modeling of joint-assembled structures," Wang introduces a novel framework that leverages physics-informed neural networks to simultaneously identify joint parameters and construct reduced-order dynamic models—a breakthrough for accurately simulating assembled structures without exhaustive experimental data. This work, already garnering 6 citations, addresses a critical bottleneck in structural health monitoring and digital twin development. Complementing this, his study on "Dynamics of flexible multi-stable origami with bio-inspired creases" (5 citations) explores how biological principles can enhance the dynamic behavior of deployable origami structures, opening new pathways for adaptive aerospace and robotic systems. Wang’s contributions are notable for their methodological rigor and practical relevance, merging machine learning with classical mechanics to solve real-world engineering challenges. His research is particularly impactful for students and engineers seeking efficient, scalable approaches to modeling complex, multi-body systems.