Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification

I Kröker, S Oladyshkin - Reliability Engineering & System Safety, 2022 - Elsevier
Various real world problems deal with data-driven uncertainty. In particular, in geophysical
applications the amount of available data is often limited, posing a challenge in the …

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

M Köppel, F Franzelin, I Kröker, S Oladyshkin… - Computational …, 2019 - Springer
A variety of methods is available to quantify uncertainties arising within the modeling of flow
and transport in carbon dioxide storage, but there is a lack of thorough comparisons …

[图书][B] Entropies and symmetrization of hyperbolic stochastic Galerkin formulations

S Gerster, M Herty - 2018 - doc.global-sci.org
Stochastic quantities of interest are expanded in generalized polynomial chaos expansions
using stochastic Galerkin methods. An application to hyperbolic differential equations does …

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins

I Colombo, F Nobile, G Porta, A Scotti… - Computer Methods in …, 2018 - Elsevier
In this work we propose an Uncertainty Quantification methodology for sedimentary basins
evolution under mechanical and geochemical compaction processes, which we model as a …

Quantifying multiple uncertainties in modelling shallow water-sediment flows: A stochastic Galerkin framework with Haar wavelet expansion and an operator-splitting …

J Li, Z Cao, AGL Borthwick - Applied Mathematical Modelling, 2022 - Elsevier
The interactive processes of shallow water flow, sediment transport, and morphological
evolution constitute a hierarchy of multi-physical problems of significant interests in a …

Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to …

R Kohlhaas, I Kröker, S Oladyshkin… - Computational …, 2023 - Springer
Surrogate models are widely used to improve the computational efficiency in various
geophysical simulation problems by reducing the number of model runs. Conventional one …

[HTML][HTML] A hybrid polynomial chaos expansion–Gaussian process regression method for Bayesian uncertainty quantification and sensitivity analysis

P Manfredi - Computer Methods in Applied Mechanics and …, 2025 - Elsevier
This paper introduces a novel hybrid method for uncertainty quantification (UQ) combining
the benefits of polynomial chaos expansion (PCE) and Gaussian process regression (GPR) …

Stochastic Modeling of Two-Phase Transport in Fractured Porous Media Under Geological Uncertainty Using an Improved Probabilistic Collocation Method

MS Sharafi, M Ahmadi, A Kazemi - SPE Journal, 2024 - onepetro.org
Simulation of multiphase transport through fractured porous media is highly affected by the
uncertainty in fracture distribution and matrix block size that arises from inherent …

Analysis of travel time distributions for uncertainty propagation in channelized porous systems

O Fuks, F Ibrahima, P Tomin, HA Tchelepi - Transport in Porous Media, 2019 - Springer
In the context of stochastic two-phase flow in porous media, one is often interested in
estimating the statistics of fluid saturations in the reservoir. In this work, we show how we can …

Probabilistic Godunov-type hydrodynamic modelling under multiple uncertainties: robust wavelet-based formulations

J Shaw, G Kesserwani, P Pettersson - Advances in Water Resources, 2020 - Elsevier
Intrusive stochastic Galerkin methods propagate uncertainties in a single model run,
eliminating repeated sampling required by conventional Monte Carlo methods. However, an …