mlsanityData to 7 Oct 2026

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

arXiv 2204.06362 ↗First posted 13 Apr 2022cs.LG cs.SD eess.AScited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 103 of its 558 references that are in the map. First posted on arXiv 13 Apr 2022.

Builds on

103 of 558 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 4; 2026: 1.

7 papers.

  1. 2025Disentangling Slow and Fast Temporal Dynamics in Degradation Inference with Hierarchical Differential Modelscited by 3
  2. 2025Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Formingcited by 1
  3. 2022On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulationscited by 0
  4. 2024Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problemscited by 0
  5. 2025ServoLNN: Lagrangian Neural Networks Driven by Servomechanismscited by 0
  6. 2025Structural System Identification via Validation and Adaptationcited by 0
  7. 2026Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decompositioncited by 0