Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Review of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
🤖 AI Summary
To address insufficient predictive robustness in structural dynamics arising from measurement noise and modeling errors, this paper systematically reviews and unifies probabilistic (Bayesian/frequentist) and non-probabilistic (interval, fuzzy) uncertainty quantification paradigms within machine learning. Focusing on forward tasks (response prediction, reliability analysis) and inverse tasks (system identification, damage diagnosis), we propose the first uncertainty-aware ML classification framework specifically tailored for structural dynamics. The framework delineates applicability boundaries and implementation pathways for key methods—including Bayesian neural networks, interval analysis, fuzzy learning, sensitivity analysis, and model updating. We further identify, for the first time, the superiority of Bayesian neural networks in capturing nonlinear mappings and joint uncertainty representations. Finally, we highlight critical research gaps and scalable directions, providing practitioners with a practical, actionable methodology selection guide and technical foundation for engineering applications.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Addressing uncertainties in machine learning for structural dynamics applications
Surveying probabilistic and non-probabilistic methods for forward and inverse problems
Enhancing reliability of ML predictions through uncertainty-aware approaches
Innovation
Methods, ideas, or system contributions that make the work stand out.
Surveying probabilistic and non-probabilistic uncertainty-aware methods
Emphasizing Bayesian neural networks for uncertainty quantification
Addressing uncertainties in structural dynamics forward and inverse problems
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