Results 21 to 30 of about 78,109 (159)
Uncertainty Quantification in Sunspot Counts [PDF]
The Astrophysical Journal, 2019 Abstract Observing and counting sunspots constitutes one of the longest-running scientific experiments, with first observations dating back to Galileo (around 1610). Today the sunspot number (SN) time series acts as a benchmark of solar activity in a large range of physical models. An appropriate statistical modeling, adapted to the time
Sophie Mathieu +4 more
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Algorithms, 2022 Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase.
Sihmehmet Yildiz +2 more
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Coupling Design and Validation Analysis of an Integrated Framework of Uncertainty Quantification
Energies, 2023 The uncertainty quantification is an indispensable part for the validation of the nuclear safety best-estimate codes. However, the uncertainty quantification usually requires the combination of statistical analysis software and nuclear reactor ...
Bo Pang +7 more
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Uncertainty quantification for Bayesian CART [PDF]
The Annals of Statistics, 2021 This work affords new insights into Bayesian CART in the context of structured wavelet shrinkage. The main thrust is to develop a formal inferential framework for Bayesian tree-based regression. We reframe Bayesian CART as a g-type prior which departs from the typical wavelet product priors by harnessing correlation induced by the tree topology.
Castillo, Ismaël, Ročková, Veronika
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Bi-level Hybrid Uncertainty Quantification in Fatigue Analysis: S-N Curve Approach
Fracture and Structural Integrity, 2020 Due to its physical complexity, fatigue phenomenon inherently presents a significant number of uncertain parameters to be predicted. In uncertainty quantification (UQ), research has demonstrated that even a small variation in uncertain input quantities ...
Raphael Basilio Pires Nonato
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UQpy v4.1: Uncertainty quantification with Python
SoftwareX, 2023 This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to ...
Dimitrios Tsapetis +11 more
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Complex model calibration through emulation, a worked example for a stochastic epidemic model
Epidemics, 2022 Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology,
Michael Dunne +12 more
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EntropyGiven the increasing global emphasis on sustainable energy usage and the rising energy demands of cellular wireless networks, this work seeks an optimal short-term, continuous-time power-procurement schedule to minimize operating expenditure and the ...
Nadhir Ben Rached +2 more
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An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change [PDF]
Earth System DynamicsThis study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario ...
J. D. Jakeman +6 more
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Principal component density estimation for scenario generation using normalizing flows
Data-Centric Engineering, 2022 Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources, such as photovoltaics (PV) and wind as well as load demands, has recently gained attention.
Eike Cramer +3 more
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