The energy crises caused by the rapidly increasing population density around the world and
the economic, environmental and health threats, that have reached significant dimensions …

In many fields, such as environmental risk assessment, agronomic system behavior,
aerospace engineering, and nuclear safety, mathematical models turned into computer code …

Titre de la présentation Page 1 Kolkata, December, 2018 Introduction to sensitivity analysis
Bertrand Iooss (EDF R&D & Institut de Mathématiques de Toulouse, France) Page 2 ➢ …

Multivariate functions encountered in high-dimensional uncertainty quantification problems
often vary most strongly along a few dominant directions in the input parameter space. We …

This paper examines the efficient variance-based global sensitivity analysis (GSA),
quantified by estimating first-/higher-order and total-effect Sobol'indices, for applications …

Dynamical earth and environmental systems models are typically computationally intensive
and highly parameterized with many uncertain parameters. Together, these characteristics …

We propose a new statistical estimation framework for a large family of global sensitivity
analysis indices. Our approach is based on rank statistics and uses an empirical correlation …

Reliability assessment in presence of epistemic uncertainty leads to consider the failure
probability as a quantity depending on the state of knowledge about uncertain input …

Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation,
entail a huge computational complexity when dealing with input-output maps involving the …

This paper presents an efficient surrogate modeling strategy for the uncertainty
quantification and Bayesian calibration of a hydrological model. In particular, a process …