Results 51 to 60 of about 12,137 (169)

Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

Frontiers in Marine Science, 2023
To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account
Mohamad Abed El Rahman Hammoud, H. V. R. Mittal, Olivier Le Maître, Ibrahim Hoteit, Omar Knio   +4 more
doaj   +1 more source

Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems

, 2014
Polynomial chaos expansions are used to reduce the computational cost in the Bayesian solutions of inverse problems by creating a surrogate posterior that can be evaluated inexpensively.
Chorin, Alexandre J., Lu, Fei, Morzfeld, Matthias, Tu, Xuemin   +3 more
core   +1 more source

Impact on signal integrity of interconnect variabilities [PDF]

, 2014
In this paper, literature results on the statistical simulation of lossy and dispersive interconnect networks with uncertain physical properties are extended to general nonlinear circuits.
Canavero, Flavio, De Zutter, Daniël, Manfredi, Paolo, Vande Ginste, Dries   +3 more
core   +1 more source

Magnetometric resistivity tomography using chaos polynomial expansion [PDF]

Geophysical Journal International, 2020
SUMMARY We present an inversion algorithm to reconstruct the spatial distribution of the electrical conductivity from the analysis of magnetometric resistivity (MMR) data acquired at the ground surface. We first review the theoretical background of MMR connecting the generation of a magnetic field in response to the injection of a low ...
Vu, M, Jardani, Abderrahim, Revil, A, Jessop, M   +3 more
openaire   +3 more sources

On polynomial chaos expansion via gradient-enhanced ℓ1-minimization [PDF]

Journal of Computational Physics, 2016
Gradient-enhanced Uncertainty Quantification (UQ) has received recent attention, in which the derivatives of a Quantity of Interest (QoI) with respect to the uncertain parameters are utilized to improve the surrogate approximation. Polynomial chaos expansions (PCEs) are often employed in UQ, and when the QoI can be represented by a sparse PCE, $\ell_1$-
Peng, Ji, Hampton, Jerrad, Doostan, Alireza   +2 more
openaire   +3 more sources

Uncertainty quantification of grating filters via a Polynomial-Chaos method with a variance-adaptive design domain

Results in Optics
In this work, we describe an algorithm based on Polynomial-Chaos (PC) expansions for the study of uncertainty quantification problems involving grating filters.
Aristeides D. Papadopoulos, Theodoros T. Zygiridis, Elias N. Glytsis, Nikolaos V. Kantartzis   +3 more
doaj   +1 more source

Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design

, 2017
In the field of uncertainty quantification, sparse polynomial chaos (PC) expansions are commonly used by researchers for a variety of purposes, such as surrogate modeling. Ideas from compressed sensing may be employed to exploit this sparsity in order to
Diaz, Paul, Doostan, Alireza, Hampton, Jerrad   +2 more
core   +1 more source

Local/global non-intrusive coupling strategy for robust design: a first attempt

Advanced Modeling and Simulation in Engineering Sciences
This work investigates how non-intrusive local/global coupling strategies can be applied in the context of robust design. The objective is to propagate uncertainties from the local to the global scale using non-intrusive techniques, in order to estimate ...
Léa Karaouni, Ludovic Chamoin, Olivier Allix, Stéphane Guinard   +3 more
doaj   +1 more source

A Posteriori Validation of Generalized Polynomial Chaos Expansions

SIAM Journal on Applied Dynamical Systems, 2023
Generalized polynomial chaos expansions are a powerful tool to study differential equations with random coefficients, allowing in particular to efficiently approximate random invariant sets associated to such equations. In this work, we use ideas from validated numerics in order to obtain rigorous a posteriori error estimates together with existence ...
openaire   +3 more sources

Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling

Nuclear Engineering and Technology, 2020
Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems.
Majdi I. Radaideh, Tomasz Kozlowski
doaj   +1 more source