On fractional moment estimation from polynomial chaos expansion
Reliability Engineering & System SafetyFractional statistical moments are utilized for various tasks of uncertainty quantification, including the estimation of probability distributions. However, an estimation of fractional statistical moments of costly mathematical models by statistical sampling is challenging since it is typically not possible to create a large experimental design due to ...
Lukáš Novák +2 more
openaire +2 more sources
Tackling random fields non-linearities with unsupervised clustering of polynomial chaos expansion in latent space: application to global sensitivity analysis of river flooding [PDF]
, 2021 Siham El Garroussi +4 more
openalex +1 more source
Sparse polynomial chaos expansion for universal stochastic kriging
Journal of Computational and Applied MathematicsSurrogate modelling techniques have opened up new possibilities to overcome the limitations of computationally intensive numerical models in various areas of engineering and science. However, while fundamental in many engineering applications and decision-making, the incorporation of uncertainty quantification into meta-models remains a challenging ...
J.C. García-Merino +2 more
openaire +4 more sources
Polynomial Chaos Expansion Based Rauch–Tung–Striebel Smoothers
2024 27th International Conference on Information Fusion (FUSION)Peer ...
Kumar Kundan, Särkkä Simo
openaire +2 more sources
EnergiesThis work investigates the geometric design and optimisation of a dynamic inductive power transfer coupler for two-wheeled electric vehicles under misalignment and magnetic-field exposure constraints.
Mário Loureiro +2 more
doaj +1 more source
A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions [PDF]
, 2017 John Jakeman, Akil Narayan, Tao Zhou
openalex +1 more source
Combining Polynomial Chaos Expansions and Kriging
, 2014 Computer simulation has emerged as a key tool for designing and assessing engineeringsystems in the last two decades. Uncertainty quantification has becomepopular more recently as a way to model all the uncertainties affecting the systemand their impact onto its performance.In this respect meta-models (a.k.a.
Schöbi, R. +3 more
openaire +1 more source
Uncertainty Quantification in Molecular Signals using Polynomial Chaos Expansion [PDF]
, 2019 Mahmoud Abbaszadeh +3 more
openalex +1 more source
Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions [PDF]
, 2018 Xiaosong Du +5 more
openalex +1 more source
ON PHYSICALLY CONSTRAINED NON-INTRUSIVE POLYNOMIAL CHAOS EXPANSION [PDF]
, 2023 Lukăš Novák +2 more
openalex +1 more source