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

arXiv 2408.08629 ↗First posted 16 Aug 2024cs.LG math.DScited by 0

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Abstract (arXiv)

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.

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References · 21

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2016Variational Inference: A Review for Statisticianscited by 1,466
  3. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  4. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  5. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  6. 2019Machine Learning for Fluid Mechanicscited by 380
  7. 2022Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisonscited by 169
  8. 2021Deep Generative Models in Engineering Design: A Reviewcited by 103
  9. 2020A Dual-Dimer Method for Training Physics-Constrained Neural Networks with Minimax Architecturecited by 50
  10. 2021Learning Functional Priors and Posteriors from Data and Physicscited by 37
  11. 2020A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applicationscited by 33
  12. 2019Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensemblescited by 30
9 more
  1. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  2. 2013Semi-supervised clustering methodscited by 18
  3. 2022A Review of Data-Driven Discovery for Dynamic Systemscited by 18
  4. 2021Active learning for structural reliability: survey, general framework and benchmarkcited by 15
  5. 2021Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertaintycited by 11
  6. 2019Bayesian deep learning with hierarchical prior: Predictions from limited and noisy datacited by 8
  7. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  8. 2023Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processescited by 5
  9. 2024Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networkscited by 2

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