Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion
S Oladyshkin, W Nowak - Reliability Engineering & System Safety, 2012 - Elsevier
We discuss the arbitrary polynomial chaos (aPC), which has been subject of research in a
few recent theoretical papers. Like all polynomial chaos expansion techniques, aPC …
few recent theoretical papers. Like all polynomial chaos expansion techniques, aPC …
Making sense of global sensitivity analyses
This study presents improved understanding of sensitivity analysis methods through a
comparison of the local sensitivity and two global sensitivity analysis methods: the Morris …
comparison of the local sensitivity and two global sensitivity analysis methods: the Morris …
Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations
MM Rajabi, B Ataie-Ashtiani, CT Simmons - Journal of Hydrology, 2015 - Elsevier
Real world models of seawater intrusion (SWI) require high computational efforts. This
creates computational difficulties for the uncertainty propagation (UP) analysis of these …
creates computational difficulties for the uncertainty propagation (UP) analysis of these …
Uncertainty quantification of medium‐term heat storage from short‐term geophysical experiments using Bayesian evidential learning
In theory, aquifer thermal energy storage (ATES) systems can recover in winter the heat
stored in the aquifer during summer to increase the energy efficiency of the system. In …
stored in the aquifer during summer to increase the energy efficiency of the system. In …
Robust subsampling ANOVA methods for sensitivity analysis of water resource and environmental models
Sensitivity analysis is an important component for modelling water resource and
environmental processes. Analysis of Variance (ANOVA), has been widely used for global …
environmental processes. Analysis of Variance (ANOVA), has been widely used for global …
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 …
The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory
Abstract Artificial Intelligence and Machine learning have been widely used in various fields
of mathematical computing, physical modeling, computational science, communication …
of mathematical computing, physical modeling, computational science, communication …
Hyporheic flows in stratified sediments: Implications on residence time distributions
A Marzadri, V Ciriello… - Water Resources …, 2024 - Wiley Online Library
The fate of nutrients and contaminants in fluvial ecosystems is strongly affected by the
mixing dynamics between surface water and groundwater within the hyporheic zone …
mixing dynamics between surface water and groundwater within the hyporheic zone …
Uncertainty quantification on the effects of rain-induced erosion on annual energy production and performance of a Multi-MW wind turbine
Wind turbine blade erosion has risen to the attention of researchers and industry lately in an
effort to keep ageing wind farms productive. Although not new, erosion-related blade …
effort to keep ageing wind farms productive. Although not new, erosion-related blade …
A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model
Abstract Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit
framework for stochastic calibration of hydrogeologic models accounting for uncertainties; …
framework for stochastic calibration of hydrogeologic models accounting for uncertainties; …