Artificial Intelligence-Driven Engineering Vibration Identification, Prediction and Control

This book lies at the intersection of engineering vibration and artificial intelligence, bridging core disciplines like mechanical engineering, civil engineering, and control science. It introduces a range of cutting-edge methods including machine learning, deep learning and reinforcement learning. It closely integrates these with the core links of engineering vibration, namely identification, prediction and control, puts forward a series of new approaches, and solves many practical engineering problems. These innovations address long-standing industry challenges of data scarcity, strong nonlinearity, and poor generalization of traditional methods. The book presents complex theories through intuitive visualizations and step-by-step technical workflows, balancing academic depth with practical operability. For readers, it offers actionable solutions for vibration identification, prediction, and control, while establishing a systematic knowledge system integrating data-driven and physics-informed modeling. It is ideal for researchers, engineers, and graduate students in vibration control, intelligent manufacturing, aerospace, civil engineering, and related fields seeking to advance their work with AI-powered technologies.