Results 21 to 30 of about 401,511 (278)
Uncertainty Quantification of Imperfect Diagnostics
Aerospace, 2023 The operable state of a system is maintained during operation, which requires knowledge of the system’s state. Technical diagnostics, as a process of accurately obtaining information about the system state, becomes a crucial stage in the life cycle of ...
Vladimir Ulansky, Ahmed Raza
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Inverse problems and uncertainty quantification [PDF]
, 2013 In a Bayesian setting, inverse problems and uncertainty quantification (UQ) - the propagation of uncertainty through a computational (forward) model - are strongly connected.
Litvinenko, Alexander +1 more
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Geoscience Data Journal, 2022 The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty.
Hendrik Paasche +4 more
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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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Nonparametric Uncertainty Quantification for Stochastic Gradient Flows [PDF]
, 2015 This paper presents a nonparametric statistical modeling method for quantifying uncertainty in stochastic gradient systems with isotropic diffusion. The central idea is to apply the diffusion maps algorithm to a training data set to produce a stochastic ...
Berry, Tyrus, Harlim, John
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Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method. [PDF]
, 2018 We report an evaluation of a semi-empirical quantum chemical method PM7 from the perspective of uncertainty quantification. Specifically, we apply Bound-to-Bound Data Collaboration, an uncertainty quantification framework, to characterize (a) variability
FRENKLACH, Michael +6 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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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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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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