Comparative assessment of two global sensitivity approaches considering model and parameter uncertainty
H Dai, Y Liu, A Guadagnini, S Yuan… - Water Resources …, 2024 - Wiley Online Library
Abstract Global Sensitivity Analysis (GSA) is key to assisting appraisal of the behavior of
hydrological systems through model diagnosis considering multiple sources of uncertainty …
hydrological systems through model diagnosis considering multiple sources of uncertainty …
On uncertainty quantification in hydrogeology and hydrogeophysics
Recent advances in sensor technologies, field methodologies, numerical modeling, and
inversion approaches have contributed to unprecedented imaging of hydrogeological …
inversion approaches have contributed to unprecedented imaging of hydrogeological …
Global sensitivity analysis using low-rank tensor approximations
In the context of global sensitivity analysis, the Sobol'indices constitute a powerful tool for
assessing the relative significance of the uncertain input parameters of a model. We herein …
assessing the relative significance of the uncertain input parameters of a model. We herein …
Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model
The study makes use of polynomial chaos expansions to compute Sobol׳ indices within the
frame of a global sensitivity analysis of hydro-dispersive parameters in a simplified vertical …
frame of a global sensitivity analysis of hydro-dispersive parameters in a simplified vertical …
Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis
JD Jakeman, MS Eldred, G Geraci… - … Journal for Numerical …, 2020 - Wiley Online Library
In this paper, we present an adaptive algorithm to construct response surface
approximations of high‐fidelity models using a hierarchy of lower fidelity models. Our …
approximations of high‐fidelity models using a hierarchy of lower fidelity models. Our …
The Sparse Grids Matlab kit--a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
C Piazzola, L Tamellini - arXiv preprint arXiv:2203.09314, 2022 - arxiv.org
The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be
used for approximating high-dimensional functions and, in particular, for surrogate-model …
used for approximating high-dimensional functions and, in particular, for surrogate-model …
Moment-based metrics for global sensitivity analysis of hydrological systems
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 …
systems. Our approach allows assessing the impact of uncertain parameters on main …
Integrating process‐based reactive transport modeling and machine learning for electrokinetic remediation of contaminated groundwater
R Sprocati, M Rolle - Water Resources Research, 2021 - Wiley Online Library
Advanced reactive transport models of fluid flow and solute transport in subsurface porous
media are instrumental for the assessment of contaminant environmental fate and for the …
media are instrumental for the assessment of contaminant environmental fate and for the …
DGSA: A Matlab toolbox for distance-based generalized sensitivity analysis of geoscientific computer experiments
Sensitivity analysis plays an important role in geoscientific computer experiments, whether
for forecasting, data assimilation or model calibration. In this paper we focus on an extension …
for forecasting, data assimilation or model calibration. In this paper we focus on an extension …
Algorithm 1040: The Sparse Grids Matlab Kit-a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
C Piazzola, L Tamellini - ACM Transactions on Mathematical Software, 2024 - dl.acm.org
The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be
used for approximating high-dimensional functions and, in particular, for surrogate-model …
used for approximating high-dimensional functions and, in particular, for surrogate-model …