Uncertainty quantification of squeal instability via surrogate modelling

2015 ◽  
Vol 60-61 ◽  
pp. 887-908 ◽  
Author(s):  
Amir Nobari ◽  
Huajiang Ouyang ◽  
Paul Bannister
Keyword(s):  
Uncertainty Quantification ◽  
Surrogate Modelling

On the Potential of a Multi-Fidelity G-POD Based Approach for Optimization and Uncertainty Quantification

2014 ◽  
Author(s):  
David J. J. Toal
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Uncertainty Quantification ◽  
Surrogate Models ◽  
Orthogonal Decomposition ◽  
High Fidelity ◽  
Computational Domain ◽  
Surrogate Modelling ◽  
Proper Orthogonal ◽  
Fidelity Model ◽  
Entire Computational Domain

Traditional multi-fidelity surrogate models require that the output of the low fidelity model be reasonably well correlated with the high fidelity model and will only predict scalar responses. The following paper explores the potential of a novel multi-fidelity surrogate modelling scheme employing Gappy Proper Orthogonal Decomposition (G-POD) which is demonstrated to accurately predict the response of the entire computational domain thus improving optimization and uncertainty quantification performance over both traditional single and multi-fidelity surrogate modelling schemes.

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell
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