Explore
Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
0 Ungluers have
Faved this Work
Login to Fave
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.
Rights Information
Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.Downloads
This work has been downloaded 42 times via unglue.it ebook links.
- 42 - pdf (CC BY-SA) at Unglue.it.
Keywords
- Distributed Parameter Systems
- generalized polynomial chaos
- Parameter estimation
- ParameterschätzungUncertainty Quantification
- Sensitivitätsanalyse
- sensitivity analysis
- Unsicherheit Quantifizierung
- verteilt-parametrische Systeme
Links
DOI: 10.5445/KSP/1000066940Editions