Derivative based global sensitivity measures and their link with global sensitivity indices

By Ilya M. Sobol, Sergei S. Kucherenko. Mathematics and Computers in Simulation, 2009

459 citations recorded by the source corpus.

Subfields: Mechanics of Materials, Numerical Analysis, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b57afbfbc2d03dbaac25dc021d4d61aa04064e20

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Citations in the corpus

SALib: An open-source Python library for Sensitivity Analysis (2017, methodology): “SALib contains Python implementations of commonly used global sensitivity analysis methods, including Sobol (Sobol’ 2001, Andrea Saltelli (2002), Andrea Saltelli et al. (2010)), Morris (Morris 1991, Campolongo, Cariboni, and Saltelli (2007)), FAST (Cukier et al. 1973, A. Saltelli, Tarantola, and Cha”
Manifold microchannel heat sink design using optimization under uncertainty (2014, methodology): “The gPC-based UQ analysis also provides other useful information such as Sobol’s indices [26].”
Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices (2017, methodology): “Other global approaches to sensitivity analysis include the elementary effects approach (Morris, 1991), global derivative-based measures (Sobol’ & Kucherenko, 2009), moment-independent methods (Da Veiga, 2015), variogram-based approaches (Razavi et al., 2019), and many others.”
Poincaré inequalities on intervals – application to sensitivity analysis (2016, unknown): “Then another global sensitivity index, called DGSM (Derivative-based Global Sensitivity Measure), can advantageously be used as a proxy [32, 21].”
Uncertainty and sensitivity decomposition of building energy models (2012, methodology): “LHS was also performed by Struck et al. (2009) to compare the performance of conceptual and detailed building models during uncertainty and sensitivity analysis (SA) (discussed further below).”
Metamodel-based sensitivity analysis: polynomial chaos expansions and Gaussian processes (2016, unknown): “is toolboxes such as OpenTURNS [2] and UQLab [36]. Finally it is worth mentioning that the so-called derivative-based global sensitivity measures (DGSM) originally introduced by Sobol’ and Kucherenko [51] can also be computed using surrogate models. In particular, polynomial chaos expansions may be ”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “…and the distributional properties of the sampled derivatives (e.g., first or second moment) to provide an average global measure of sensitivity (see e.g., Morris, 1991; Campo-longo et al., 2007; Sobol ’ and Kucherenko, 2009; Campolongo et al., 2011; Lamboni et al., 2013; Rakovec et al., 2014).”
Explainable Neural Networks based on Additive Index Models (2018, methodology): “There are, broadly speaking, three inter-related model–based areas of research: a) global diagnostics (Sobol & Kucherenko (2009), Kucherenko (2010)); b) local diagnostics ( Sundararajan et al. (2017), Ancona et al. (2018)); and c) development of approximate or surrogate models that may be easier to…”
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) (2018, unknown): “…(Friedman & Popescu, 2005), related ones such as ICE (Goldstein, Kapelner, Bleich, & Pitkin, 2013) and ALE plots (Apley, 2016), derivative-based analyses (Kucherenko, 2010; Sobol & Kucherenko, 2009), Sobol indices (Iooss & Lemaitre, 2014), Shapley analyses (Lundberg & Lee, 2017), and so on.”
Derivative based global sensitivity measures (2014, methodology): “In [2] we suggested to use a similar functional, which we called derivative based global sensitivity measure 2”
From screening to quantitative sensitivity analysis. A unified approach (2011, methodology): “A possible way to overcome this problem is to include multi-dimensional averaging of local measures [18,16,38,39].”