Variance-based sensitivity analysis of model outputs using surrogate models

By Davood Shahsavani, Anders Grimvall. Environmental Modelling & Software, 2011

370 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0e96a0f582fc975b4f23e34226e6ebff4249fbb7

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Citations in the corpus

A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, methodology): “A common formulation when using Sobol sequence leads to the following result [107]:”
From screening to quantitative sensitivity analysis. A unified approach (2011, methodology): “Different designs have been developed in order to compute efficiently sensitivity indices, spanning from the well known approach of the Latin hypercube [17,10] to the recently design developed in [30].”
Characterising performance of environmental models (2013, methodology): “For more information on sensitivity analysis, see Saltelli et al. (2000), Norton (2008), Frey and Patil (2002), Saltelli and Annoni (2010), Shahsavani and Grimvall (2011), Makler-Pick et al. (2011), Yang (2011), Nossent et al. (2011), Ratto et al. (2012) and Ravalico et al. (2010).”
From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass (2019, methodology): “A global sensitivity analysis was conducted using a variance based sensitivity metric (Saltelli et al., 2010) to investigate how the different input parameters of the biomass model affected the total biomass.”
Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques (2015, methodology): “Sampling based computational strategies to compute these measures are discussed in [33,34].”
Ratings and rankings: voodoo or science? (2011, background): “Si can be estimated from design points: Sobol’ (1993); Saltelli (2002); Saltelli et al. (2010); Fourier analysis: Tarantola et al. (2006); Plischke (2010); Xu and Gertner (2011), or others.”
A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model (2014, background): “More recently, along with the development of response surface methods (RSM), SA based on RSM makes it cheaper for estimating parameter effects (Ratto et al., 2007; Shahsavani and Grimvall, 2011).”
Density modification-based reliability sensitivity analysis (2012, methodology): “This effect is well-known in SI estimation problems and can be corrected by improved formulas [15].”
SALib: An open-source Python library for Sensitivity Analysis (2017, methodology): “…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 Chan…”
SAMPLING BASED ON SOBOL 0 SEQUENCES FOR MONTE CARLO TECHNIQUES APPLIED TO BUILDING SIMULATIONS (2011, methodology): “The discrepancy metric was defined by Ilya M. Sobol′ and is the maximum deviation between the theoretical density dt = 1/N and the point density di in an arbitrary hyper-parallelepiped (Pi) within the parameter space (hypercube) (Saltelli et al., 2010).”