scholarly journals Bayesian sparse polynomial chaos expansion for global sensitivity analysis

2017 ◽  
Vol 318 ◽  
pp. 474-496 ◽  
Author(s):  
Qian Shao ◽  
Anis Younes ◽  
Marwan Fahs ◽  
Thierry A. Mara
Keyword(s):  
Sensitivity Analysis ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Global Sensitivity ◽  
Sparse Polynomial ◽  
Sparse Polynomial Chaos Expansion

scholarly journals A Global Sensitivity Analysis Framework for Hybrid Simulation

2021 ◽  
Author(s):  
Giuseppe Abbiati ◽  
Stefano Marelli ◽  
Nikolaos Tsokanas ◽  
Bruno Sudret ◽  
Bozidar Stojadinovic
Keyword(s):  
Sensitivity Analysis ◽  
Hybrid Model ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Hybrid Simulation ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Analysis Framework ◽  
Global Sensitivity ◽  
Sensitivity Indices

Hybrid Simulation is a dynamic response simulation paradigm that merges physical experiments and computational models into a hybrid model. In earthquake engineering, it is used to investigate the response of structures to earthquake excitation. In the context of response to extreme loads, the structure, its boundary conditions, damping, and the ground motion excitation itself are all subjected to large parameter variability. However, in current seismic response testing practice, Hybrid Simulation campaigns rely on a few prototype structures with fixed parameters subjected to one or two ground motions of different intensity. While this approach effectively reveals structural weaknesses, it does not reveal the sensitivity of structure's response. This thus far missing information could support the planning of further experiments as well as drive modeling choices in subsequent analysis and evaluation phases of the structural design process.This paper describes a Global Sensitivity Analysis framework for Hybrid Simulation. This framework, based on Sobol' sensitivity indices, is used to quantify the sensitivity of the response of a structure tested using the Hybrid Simulation approach due to the variability of the prototype structure and the excitation parameters. Polynomial Chaos Expansion is used to surrogate the hybrid model response. Thereafter, Sobol' sensitivity indices are obtained as a by-product of polynomial coefficients, entailing a reduced number of Hybrid Simulations compared to a crude Monte Carlo approach. An experimental verification example highlights the excellent performance of Polynomial Chaos Expansion surrogates in terms of stable estimates of Sobol' sensitivity indices in the presence of noise caused by random experimental errors.

Global sensitivity analysis through polynomial chaos expansion of a basin-scale geochemical compaction model

2012 ◽  
Vol 17 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Luca Formaggia ◽  
Alberto Guadagnini ◽  
Ilaria Imperiali ◽  
Valentina Lever ◽  
Giovanni Porta ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Polynomial Chaos ◽  
Global Sensitivity Analysis ◽  
Polynomial Chaos Expansion ◽  
Chaos Expansion ◽  
Global Sensitivity ◽  
Basin Scale
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