Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
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 analy...
Saved in:
Superior document: | Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe |
---|---|
: | |
Year of Publication: | 2017 |
Language: | English |
Series: | Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe
|
Physical Description: | 1 electronic resource (XIX, 210 p. p.) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Nonlinear state and parameter estimation of spatially distributed systems
by: Sawo, Felix
Published: (2009) -
Parameter Estimation and Uncertainty Quantification in Water Resources Modeling
by: Renard, Philippe
Published: (2020) -
Error estimates for distributed parameter identification problems / Tommi Kärkkäinen
by: Kärkkäinen, Tommi
Published: (1995) -
Parameter identification and monitoring of mechanical systems under nonlinear vibration / / Juan Carlos Jauregui.
by: Jauregui, Juan Carlos,
Published: (2014.) -
Bayesian analysis with Python : : unleash the power and flexibility of the Bayesian framework / / Osvaldo Martin.
by: Martin, Osvaldo,
Published: (2016.)