A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection

SummaryIn the present study, a general dynamic data‐driven application system (DDDAS) is developed for real‐time monitoring of damage in composite materials using methods and models that account for uncertainty in experimental data, model parameters, and in the selection of the model itself. The met...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	International journal for numerical methods in engineering 2015-04, Vol.102 (3-4), p.379-403
Hauptverfasser:	Prudencio, E. E., Bauman, P. T., Faghihi, D., Ravi-Chandar, K., Oden, J. T.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis Bayesian model selection Calibration Damage dynamic data-driven application systems Dynamical systems Dynamics extended Kalman filter Infrastructure material damage Materials selection Mathematical models Monitoring
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!