In the era of big data, machine learning (ML) has become a powerful tool in various fields, notably impacting structural dynamics. ML algorithms offer advantages by modeling physical phenomena based on data, even in the absence of underlying mechanisms. However, uncertainties such as measurement noise and modeling errors can compromise the reliability of ML predictions, highlighting the need for effective uncertainty awareness to enhance prediction robustness. This paper presents a comprehensive review on navigating uncertainties in ML, categorizing uncertainty-aware approaches into probabilistic methods (including Bayesian and frequentist perspectives) and non-probabilistic methods (such as interval learning and fuzzy learning). Bayesian neural networks, known for their uncertainty quantification and nonlinear mapping capabilities, are emphasized for their superior performance and potential. The review covers various techniques and methodologies for addressing uncertainties in ML, discussing fundamentals and implementation procedures of each method. While providing a concise overview of fundamental concepts, the paper refrains from in-depth critical explanations. Strengths and limitations of each approach are examined, along with their applications in structural dynamic forward problems like response prediction, sensitivity assessment, and reliability analysis, and inverse problems like system identification, model updating, and damage identification. Additionally, the review identifies research gaps and suggests future directions for investigations, aiming to provide comprehensive insights to the research community. By offering an extensive overview of both probabilistic and non-probabilistic approaches, this review aims to assist researchers and practitioners in making informed decisions when utilizing ML techniques to address uncertainties in structural dynamic problems.

本研究解决了机器学习在结构动态预测中由于测量噪声和建模误差导致的可靠性问题，强调了不确定性意识的重要性。文章通过分类概率方法与非概率方法，对应对不确定性的各种技术进行了综合评审，特别强调了贝叶斯神经网络在性能与潜力上的优势。研究的最大贡献在于识别了研究缺口并建议未来研究方向，为学术界和实践者提供了全面的见解。

Navigating Uncertainties in Machine Learning for Structural Dynamics: A
  Comprehensive Review of Probabilistic and Non-Probabilistic Approaches in
  Forward and Inverse Problems

机器学习在结构动态中的不确定性导航：前向与逆向问题中的概率与非概率方法的综合评审