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Atmosphere, 2023 Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi ...
Yuanpu Liu +5 more
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Global sensitivity analysis with dependence measures [PDF]
Journal of Statistical Computation and Simulation, 2014 Global sensitivity analysis with variance-based measures suffers from several theoretical and practical limitations, since they focus only on the variance of the output and handle multivariate variables in a limited way. In this paper, we introduce a new class of sensitivity indices based on dependence measures which overcomes these insufficiencies ...
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A Global Sensitivity Analysis Framework for Hybrid Simulation [PDF]
Mechanical Systems and Signal Processing, 2021 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 ...
Stojadinovic, Bozidar +4 more
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Global sensitivity analysis of parameters in DRAINMOD-S
Shui kexue jinzhan, 2015 In order to efficiently select the optimized parameters in DRAINMOD-S and figure out how the parametric variation influences the simulation results,sensitivity analysis of the parameters in the model was performed. Taking the pipe drainage desalting test
YU Shuang'en +3 more
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Quantile-oriented global sensitivity analysis of design resistance
Journal of Civil Engineering and Management, 2019 The article investigates the application of a new type of global quantile-oriented sensitivity analysis (called QSA in the article) and contrasts it with established Sobol’ sensitivity analysis (SSA). Comparison of QSA of the resistance design value (0.1
Zdeněk Kala
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Global Sensitivity Analysis for Optimization with Variable Selection [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2019 The optimization of high dimensional functions is a key issue in engineering problems but it frequently comes at a cost that is not acceptable since it usually involves a complex and expensive computer code. Engineers often overcome this limitation by first identifying which parameters drive the most the function variations: non-influential variables ...
Spagnol, Adrien +2 more
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Sobol tensor trains for global sensitivity analysis [PDF]
Reliability Engineering & System Safety, 2019 Sobol indices are a widespread quantitative measure for variance-based global sensitivity analysis, but computing and utilizing them remains challenging for high-dimensional systems. We propose the tensor train decomposition (TT) as a unified framework for surrogate modeling and global sensitivity analysis via Sobol indices.
Rafael Ballester-Ripoll +2 more
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Global Sensitivity Analysis for Statistical Model Parameters [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2019 revisions
Joseph L. Hart +2 more
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Moment-based metrics for global sensitivity analysis of hydrological systems [PDF]
Hydrology and Earth System Sciences, 2017 We propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output,
A. Dell'Oca +4 more
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A Review on Global Sensitivity Analysis Methods [PDF]
, 2015 This chapter makes a review, in a complete methodological framework, of various global sensitivity analysis methods of model output. Numerous statistical and probabilistic tools (regression, smoothing, tests, statistical learning, Monte Carlo, \ldots) aim at determining the model input variables which mostly contribute to an interest quantity depending
Iooss, Bertrand, Lemaître, Paul
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