A multi-method Generalized Global Sensitivity Matrix approach to accounting for the dynamical nature of earth and environmental systems models

2019 ◽  
Vol 114 ◽  
pp. 1-11 ◽  
Author(s):  
Saman Razavi ◽  
Hoshin V. Gupta
Keyword(s):  
Matrix Approach ◽  
Sensitivity Matrix ◽  
Global Sensitivity ◽  
Environmental Systems ◽  
Dynamical Nature ◽  
Systems Models

Global Sensitivity Matrix for Vehicle Development

ATZ worldwide ◽  
2021 ◽  
Vol 123 (3) ◽  
pp. 26-31
Author(s):  
Jana Büttner ◽  
Stefan Schwarz ◽  
Axel Schumacher ◽  
Thomas Bäck
Keyword(s):  
Sensitivity Matrix ◽  
Vehicle Development ◽  
Global Sensitivity

scholarly journals Free-Interface Modal Synthesis Based Substructural Damage Detection Method

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Shanghong Chen ◽  
Wei Lin ◽  
Jiexin Yu ◽  
Ai Qi
Keyword(s):  
Detection Method ◽  
Synthesis Method ◽  
Eigenvalues And Eigenvectors ◽  
Sensitivity Matrix ◽  
Free Interface ◽  
Global Sensitivity ◽  
Modal Synthesis ◽  
Sensitivity Method ◽  
Derivatives Of ◽  
Storey Building

Free-interface modal synthesis method is applied to civil structure, and a substructure method is proposed by introducing the method into global sensitivity method. The substructure expression of the derivatives of eigenvalues and eigenvectors with respect to elemental parameters is obtained. The accuracy of the application of free-interface modal synthesis method is evaluated with different retained modes in substructure, and then the effectiveness of the proposed substructure sensitivity method is illustrated through an 11-storey building under both single- and multidamage cases. Both the damage locations and the extent can be effectively identified. By comparing it with the identical results of global sensitivity method, the proposed method can be faster in detecting the damage location and more stable under multidamage cases. Since this substructure sensitivity method only needs to update sensitivity matrix in the substructure with relative small number of DOFs, it may save much computation effort and become more efficient.

scholarly journals What do we do with model simulation crashes? Recommendations for global sensitivity analysis of earth and environmental systems models

2019 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Saman Razavi ◽  
Amin Haghnegahdar
Keyword(s):  
Sensitivity Analysis ◽  
Sample Size ◽  
Response Surface ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Model Simulation ◽  
Surface Modelling ◽  
Global Sensitivity ◽  
Environmental Systems ◽  
Response Surface Modelling

Abstract. Complex, software-intensive, technically advanced, and computationally demanding models, presumably with ever-growing realism and fidelity, have been widely used to simulate and predict the dynamics of the Earth and environmental systems. The parameter-induced simulation crash (failure) problem is typical across most of these models, despite considerable efforts that modellers have directed at model development and implementation over the last few decades. A simulation failure mainly occurs due to the violation of the numerical stability conditions, non-robust numerical implementations, or errors in programming. However, the existing sampling-based analysis techniques such as global sensitivity analysis (GSA) methods, which require running these models under many configurations of parameter values, are ill-equipped to effectively deal with model failures. To tackle this problem, we propose a novel approach that allows users to cope with failed designs (samples) during the GSA, without knowing where they took place and without re-running the entire experiment. This approach deems model crashes as missing data and uses strategies such as median substitution, single nearest neighbour, or response surface modelling to fill in for model crashes. We test the proposed approach on a 10-paramter HBV-SASK rainfall-runoff model and a 111-parameter MESH land surface-hydrology model. Our results show that response surface modelling is a superior strategy, out of the data filling strategies tested, and can scale well to the dimensionality of the model, sample size, and the ratio of number of failures to the sample size. Further, we conduct a "failure analysis" and discuss some possible causes of the MESH model failure.

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