Model Validation and Uncertainty Quantification, Vol. 3 - Platz, Roland; Flynn, Garrison; Neal, Kyle;(ed.) - Prospero Internet Bookshop

 
Product details:

ISBN13:9783031688928
ISBN10:3031688929
Binding:Hardback
No. of pages:146 pages
Size:279x210 mm
Language:English
Illustrations: 15 Illustrations, black & white; 73 Illustrations, color
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Model Validation and Uncertainty Quantification, Vol. 3

Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024
 
Edition number: 2025
Publisher: Springer
Date of Publication:
Number of Volumes: 1 pieces, Book
 
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Short description:

Model Validation and Uncertainty Quantification, Volume 3:  Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics, 2024, the third volume of ten from the Conference brings together contributions to this important area of research and engineering.  The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:




  • Uncertainty Quantification in Dynamics

  • Fusion of Test and Analysis

  • Model Form Uncertainty: Round Robin Challenge UQVI (Uncertainty Quantification in Vibration Isolation)

  • Recursive Bayesian System Identification

  • Virtual Sensing & Realtime Monitoring

  • Surrogate Modeling and Reduced Order Models

Long description:

Model Validation and Uncertainty Quantification, Volume 3:  Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics, 2024, the third volume of ten from the Conference brings together contributions to this important area of research and engineering.  The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on:




  • Uncertainty Quantification in Dynamics

  • Fusion of Test and Analysis

  • Model Form Uncertainty: Round Robin Challenge UQVI (Uncertainty Quantification in Vibration Isolation)

  • Recursive Bayesian System Identification

  • Virtual Sensing & Realtime Monitoring

  • Surrogate Modeling and Reduced Order Models

Table of Contents:

1. Time-Normalized Unitless Metrics for Quantifying the Value of an SHM System Throughout the Structure's Lifecycle.- 2. A Structured Knowledge Graph for a Geometric and Behavioral Digital Twin in the Context of Modal Testing.- 3. Propagation of Systematic Sensor Errors into the Frequency Domain - A Matlab Software Framework.- 4. Tutorial and Application of Bayesian Statistics on Assessing Model Form Uncertainty in Vibration Isolation.- 5. Physics-informed Information Field Theory Approach to Dynamical System Parameter and State Estimation in Path Space.- 6. Efficient Frequency-based Modelling of Rotating Tire Dynamics for NVH Applications.- 7. Population-based Mode Shape Identification of Structures via Graph Neural Networks.- 8. Stochastic Model Correction for the Adaptive Vibration Isolation Round-Robin Challenge.- 9. Spectral Model Fusion for Input Identification.- 10. Multiscale Corrosion Damage Diagnostics and Prognostics for a Miter Gate.- 11. Analyzing the Influential Factors on ICaF Performance in Bayesian Model Calibration and Forecasting.- 12. Identification of Railway Bridge Modal Properties via Acceleration Data from Traversing Trains.- 13. Propagation of Geometric Uncertainties Through the Analytic Derivative of the System Matrices.- 14. Physics-Informed Model Order Reduction via Generalized Characteristic Value Decomposition.- 15. Tribo-Dynamics Digital Twins (TDDT): Prediction of Friction and Frequency Response Function (FRF) in a Dry Sliding Tribological Contact.- 16. Dynamic State Estimation via Likelihood-Free Bayesian Inference Based on Conditional Invertible Neural Networks.- 17. Bayesian Decision-theoretic Model Selection for Monitored Systems.- 18. An Elephant in the Room: Forecasting Using Validated Physics-based Simulations.- 19. Linearization and Nonlinear Model Reduction for the Model Predictive Control of Nonlinear Structure Vibrations.- 20. Uncertainty Quantification for Deep Learning-Based Automatic Crack Detection in the Underwater Environment.- 21. Model Class and Parameter Selection for Bayesian Filtering with Application to a Modular Active Spring-Damper System: Round-Robin Challenge.- 22. Implementation of Bayesian Model Updating in Five-Story Building using Different Observations.