Probabilistic analysis of highly nonlinear models by adaptive sparse polynomial chaos: Transient infiltration in unsaturated soil
Polynomial chaos expansion (PCE) is widely adopted in geotechnical engineering as a
surrogate model for probabilistic analysis. However, the traditional low-order PCE may be …
surrogate model for probabilistic analysis. However, the traditional low-order PCE may be …
A nonintrusive reduced-order model for uncertainty quantification in numerical solution of one-dimensional free-surface water flows over stochastic beds
Free-surface water flows over stochastic beds are complex due to the uncertainties in
topography profiles being highly heterogeneous and imprecisely measured. In this study …
topography profiles being highly heterogeneous and imprecisely measured. In this study …
Parameter Identification of Fractional Order Partial Differential Equation Model Based on Polynomial–Fourier Method
C Han, Q Serra, H Laurent, É Florentin - International Journal of Applied …, 2024 - Springer
Parameter identification plays a crucial role in the study of dynamics for fractional order
structural models. This article introduces an innovative approach to identify parameters by …
structural models. This article introduces an innovative approach to identify parameters by …
Hybrid Polynomial Chaos Expansion and proper generalized decomposition approach for uncertainty quantification problems in the frame of elasticity
Y Wei, F Vazeille, Q Serra, E Florentin - Finite Elements in Analysis and …, 2022 - Elsevier
Abstract Polynomial Chaos Expansion (PCE) is a powerful and flexible metamodeling
technique, but suffers from the fact that the number of required training samples grows …
technique, but suffers from the fact that the number of required training samples grows …
Verification of polynomial chaos surrogates in the framework of structural vibrations with uncertainties
Q Serra, E Florentin - Mechanics & Industry, 2023 - mechanics-industry.org
Surface response models, such as polynomial chaos Expansion, are commonly used to deal
with the case of uncertain input parameters. Such models are only surrogates, so it is …
with the case of uncertain input parameters. Such models are only surrogates, so it is …
Methods for minimizing the error in selecting a useful signal in the presence of the «flip» effect of the approximating function
VI Marchuk, GL Hripkov, IO Nikishin… - IOP Conference Series …, 2021 - iopscience.iop.org
The paper discusses the issues of minimizing the error in selecting a useful signal in the
presence of the" flip" effect of the approximating function, which are solved by compensating …
presence of the" flip" effect of the approximating function, which are solved by compensating …
Improving the precision of the selection of piecewise linear useful signal component under conditions of a priori uncertainty
IS Shrayfel, VI Marchuk, AB Mihailov… - IOP Conference …, 2021 - iopscience.iop.org
The paper is devoted to the analytical substantiation of a new approach to the processing of
signals representing a collection of some piecewise linear signals under conditions of a …
signals representing a collection of some piecewise linear signals under conditions of a …