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.

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.

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.