The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD\&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD\&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD\&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD\&V and guides the reader to an advanced understanding of progress and prospects in the field.

翻译：机器学习（ML）的应用已迅速扩展到多个领域，并在结构动力学与振动声学（SD&V）中获得了广泛实践。得益于前所未有的数据可用性、算法进步以及计算能力的提升，机器学习从数据中提取洞察的能力不断增强，从而优化了决策制定、不确定性处理、模式识别与实时评估。SD&V领域的三大主要应用已受益于这些优势：在结构健康监测中，基于机器学习的检测与预测可实现安全运行与优化维护计划；在主动噪声控制与主动振动控制中，机器学习技术推动了系统辨识与控制设计的发展；此外，基于机器学习的替代模型为高成本仿真提供了快速替代方案，实现了鲁棒且优化的产品设计。尽管该领域已有大量研究成果，但尚未得到系统性的回顾与分析。为追踪并理解这一跨领域融合进程，本文对机器学习在SD&V分析中的应用进行了综述，揭示了当前实施状态与新兴机遇。针对上述三大应用，本文识别了其主要方法论、优势、局限性以及基于科学知识的建议。此外，论文探讨了数字孪生与物理引导机器学习在克服当前挑战、推动未来研究进展中的作用。本综述全面概述了机器学习在SD&V领域的应用现状，并引导读者深入理解该领域的进展与前景。