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 …
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 …
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
Stochastic quantities of interest are expanded in generalized polynomial chaos expansions
using stochastic Galerkin methods. An application to hyperbolic differential equations does …
using stochastic Galerkin methods. An application to hyperbolic differential equations does …
Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins
In this work we propose an Uncertainty Quantification methodology for sedimentary basins
evolution under mechanical and geochemical compaction processes, which we model as a …
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 …
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 …
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) …
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 …
uncertainty in fracture distribution and matrix block size that arises from inherent …
Analysis of travel time distributions for uncertainty propagation in channelized porous systems
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 …
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
Intrusive stochastic Galerkin methods propagate uncertainties in a single model run,
eliminating repeated sampling required by conventional Monte Carlo methods. However, an …
eliminating repeated sampling required by conventional Monte Carlo methods. However, an …