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35th IMAC, a Conference and Exposition on Structural Dynamics 2017. Proceedings. Vol.3: Model validation and uncertainty quantification. Preface

 
: Barthorpe, R.; Platz, R.; Lopez, I.; Moaveni, B.; Papadimitriou, C.

Barthorpe, R. ; Society for Experimental Mechanics -SEM-, Bethel:
35th IMAC, a Conference and Exposition on Structural Dynamics 2017. Proceedings. Vol.3: Model validation and uncertainty quantification : Garden Grove, California, January 30-February 2, 2017
Cham: Springer International Publishing, 2017 (Conference proceedings of the Society for Experimental Mechanics series)
ISBN: 978-3-319-54857-9 (Print)
ISBN: 978-3-319-54858-6 (Online)
pp.V
Conference and Exposition on Structural Dynamics <35, 2017, Garden Grove/Calif.>
English
Conference Paper
Fraunhofer LBF ()

Abstract
Model Validation and Uncertainty Quantification represents one of ten volumes of technical papers presented at the 35th IMAC, A Conference and Exposition on Structural Dynamics, organized by the Society for Experimental Mechanics and held in Garden Grove, California, on January 30–February 2, 2017. The full proceedings also include the following volumes: Nonlinear Dynamics; Dynamics of Civil Structures; Dynamics of Coupled Structures; Sensors and Instrumentation; Special Topics in Structural Dynamics; Structural Health Monitoring & Damage Detection; Rotating Machinery, Hybrid Test Methods, Vibro-Acoustics and Laser Vibrometry; Shock & Vibration, Aircraft/Aerospace, and Energy Harvesting; and Topics in Modal Analysis & Testing.
Each collection presents early findings from experimental and computational investigations on an important area within structural dynamics. Model Validation and Uncertainty Quantification (MVUQ) is one of these areas. Modeling and simulation are routinely implemented to predict the behavior of complex dynamical systems. These tools powerfully unite theoretical foundations, numerical models, and experimental data which include associated uncertainties and errors. The field of MVUQ research entails the development of methods and metrics to test model prediction accuracy and robustness while considering all relevant sources of uncertainties and errors through systematic comparisons against experimental observations.
The organizers would like to thank the authors, presenters, session organizers, and session chairs for their participation in this track.

: http://publica.fraunhofer.de/documents/N-502929.html