Results 271 to 280 of about 44,636 (300)

Uncertainty quantification of CO2 emission reduction for maritime shipping

Energy Policy, 2016
Abstract The International Maritime Organization (IMO) has recently proposed several operational and technical measures to improve shipping efficiency and reduce the greenhouse gases (GHG) emissions. The abatement potentials estimated for these measures have been further used by many organizations to project future GHG emission reductions and plot ...
Jun Yuan, Szu Hui Ng, Weng Sut Sou
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Use of Emulator Methodology for Uncertainty-Reduction Quantification

Journal of Petroleum Technology, 2016
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 169405, “Use of Emulator Methodology for Uncertainty-Reduction Quantification,” by C. Ferreira, Universidade Estadual de Campinas; I. Vernon, Durham University; D.J. Schiozer, SPE, Universidade Estadual de Campinas; and M.
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Model Reduction and Interpolation Methods in Uncertainty Quantification

SEG Technical Program Expanded Abstracts 2012, 2012
To properly answer these questions, one must characterize and quantify the uncertainties inherent in the model and its predictions. The last decade has seen the emergence and growth of methods and procedures for uncertainty quantification for computer simulations.
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Multi-Frequency Model Reduction for Uncertainty Quantification in Computational Vibroacoustics of Automobiles

SAE International Journal of Advances and Current Practices in Mobility, 2020
<div class="section abstract"><div class="htmlview paragraph">This paper deals with the vibroacoustics of complex systems over a broad frequency band of analysis. The considered system is composed of a complex structure coupled with an internal acoustic cavity.
Reyes, Justin, Gagliardini, Laurent, Desceliers, Christophe, Soize, Christian   +3 more
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Quantification of Uncertainty Reduction by Conditioning to Dynamic Production Data

ECMOR VI - 6th European Conference on the Mathematics of Oil Recovery, 1998
Making decisions in reservoir management requires a method for quantifying uncertainty. In reservoir volumetrics, expectation curves for quantifying reservoir properties such as gross rock volume, average porosity, are weIl accepted. Using Monte Carlo simulation, the uncertainty in average reservoir properties are propagated to uncertainty in ...
F. J. T. Floris, C. F. M. Bos
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Uncertainty quantification for seismic response using dimensionality reduction‐based stochastic simulator

Earthquake Engineering and Structural Dynamics
This paper introduces a stochastic simulator for seismic uncertainty quantification, which is crucial for performance-based earthquake engineering. The proposed simulator extends the recently developed dimensionality reduction-based surrogate modeling ...
Jungho Kim, Ziqi Wang
exaly   +2 more sources

Uncertainty Quantification and Reduction in the Structural Analysis of Existing Concrete Gravity Dams

2020
The failure of a large gravity dam might have catastrophic effects putting at risk human lives, not counting the considerable economic consequences. Most of dams are located in natural hazard prone areas so the structural control and the evaluation of the dam fragility (in particular against to flood and earthquake) assume great importance both to ...
Anna De Falco, Giacomo Sevieri, Giovanni Marmo   +2 more
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Stochastic model order reduction in uncertainty quantification of composite structures

Composite Structures, 2015
Abstract Multilayer fibre reinforced composites exhibit significant spatial variabilities in their response due to the variations in the individual lamina properties. Uncertainty quantification of these structures can be carried out using the polynomial chaos based stochastic finite element method (SFEM).
P. Sasikumar, R. Suresh, Sayan Gupta
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Effect of Dimensionality Reduction on Uncertainty Quantification in Trustworthy Machine Learning

2023 International Conference on Machine Learning and Cybernetics (ICMLC), 2023
Yen-Chen Li, Justin Zhan
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Uncertainty Quantification and Uncertainty Reduction Techniques for Large-scale Simulations

2014
Ph. D.
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