Robust model updating with insufficient data

Goller, B.; Pradlwarter, H. J.; Schuëller, Gerhart I.

doi:10.1016/j.cma.2009.05.009

Cited by 29 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be also noted that different concepts might be considered in this context as well. One such example is to replace the individual samples (measurements) with kernel densities, which has been applied within the validation challenge workshop both for the static [61] and dynamic examples [39]. This approach is useful especially if the concern is not to fit the optimum model to the data, but rather to establish a representation, which would be determined according to some predefined confidence levels.…”

Section: Discussionmentioning

confidence: 98%

On the Capabilities of the Polynomial Chaos Expansion Method within SFE Analysis—An Overview

Panayirci

Schuëller

2011

Arch Computat Methods Eng

View full text Add to dashboard Cite

This paper addresses the most recent developments concerning the application of the P-C expansion method within the Stochastic Finite Element (SFE) analysis, in particular considering computational solid mechanics. More specifically, the focus has been on the use of the method for the propagation of the stochastic structural responses due to the extensive amount of contributions in this context. Numerical examples presented in the literature are listed in this regard, in order to shed some light on the range of applications, especially in terms of the probabilistic dimensionality of the problem. Furthermore, the recently emerging utilization of the method within the modeling of uncertain input parameters is covered briefly. With this contribution, it is aimed to present an overview on the state-ofart of the P-C literature and the capabilities of the method.

show abstract

Section: Discussionmentioning

confidence: 98%

On the Capabilities of the Polynomial Chaos Expansion Method within SFE Analysis—An Overview

Panayirci

Schuëller

2011

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Adhikari and Friswell [6] used a sensitivity approach and the Karhunen-Loève expansion to represent distributed parameters. Goller et al [7] used Gaussian kernel densities derived from sparse modal data for the quantification of design insensitivity for robustness. Mthembu et al [8] used Bayesian evidence for model selection from a finite set of candidate parameter groups based on plausibility.…”

Section: Introductionmentioning

confidence: 99%

A comparison of two stochastic model updating methods using the DLR AIRMOD test structure

Govers

Khodaparast

Link

et al. 2015

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

“…An overview on computational methods in optimization considering uncertainties can be found in [65]. Robust updating and robust design developments with uncertain system parameters can be found in [10,38,57,58,59,82] while robust updating or robust design optimization with modeling errors taken into account with nonparametric probabilistic approach can be found in [19,20,61,77].…”

Section: Comments Concerning Robust Updating and Robust Designmentioning

confidence: 99%

Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

Soize

2013

Computers & Structures

View full text Add to dashboard Cite

To cite this version:Christian Soize. Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges. Computers and Structures, Elsevier, 2013, 126 (-) AbstractThe paper is devoted to the modeling and identification of uncertainties in computational structural dynamics for low-and medium-frequency ranges. A complete methodology is presented for the identification procedure. The first eigenfrequencies are used to quantify the uncertainties in the low-frequency band while the frequency response functions are used to quantify the uncertainties in the medium-frequency band. The system-parameter uncertainties are taken into account with the parametric probabilistic approach. The model uncertainties are taken in to account with the nonparametric probabilistic approach. The posterior stochastic model of systemparameter uncertainties is identified using the Bayes method.

show abstract

Robust model updating with insufficient data

Cited by 29 publications

References 27 publications

On the Capabilities of the Polynomial Chaos Expansion Method within SFE Analysis—An Overview

On the Capabilities of the Polynomial Chaos Expansion Method within SFE Analysis—An Overview

A comparison of two stochastic model updating methods using the DLR AIRMOD test structure

Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

Contact Info

Product

Resources

About