Uncertainty quantification for deep learning in ultrasonic crack characterization [PDF]
, 2022 Deep learning for nondestructive evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis.
Wilcox, Paul D. +3 more
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Uncertainty Quantification of Reactivity Scales [PDF]
, 2021 According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants to as few as three reactivity parameters.
Kircher, Johannes +3 more
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Uncertainty quantification and propagation with probability boxes
Nuclear Engineering and Technology, 2021 In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field.
L. Duran-Vinuesa, D. Cuervo
doaj +1 more source
jrreimer/Uncertainty-quantification-for-ecological-models-with-random-parameters: v1.0.0
, 2022 Scripts and supporting files used in manuscript "Uncertainty quantification for ecological models with random parameters.
jrreimer
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Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface [PDF]
, 2021 This paper presents an adaptive Kriging based method to perform uncertainty quantification (UQ) of the photoelectron sheath and dust levitation on the lunar surface.
Hu, Zhen +5 more
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An Accurate Sample Rejection Estimator of the Outage Probability With Equal Gain Combining
IEEE Open Journal of the Communications Society, 2020 We evaluate the outage probability (OP) for L-branch equal gain combining (EGC) receivers operating over fading channels, i.e., equivalently the cumulative distribution function (CDF) of the sum of the L channel envelopes.
Nadhir Ben Rached +3 more
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Multidimensional integration using machine learning and Monte Carlo methods for acoustic predictions [PDF]
ESAIM: Proceedings and SurveysTo predict underwater noise radiated by a ship, various numerical methods are available. In underwater acoustics, the most effective prediction methods consist in solving an acoustic analogy using an integral formulation.
Coiffard Théo +5 more
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Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
, 2022 We introduce a class of acquisition functions for sample selection that lead to faster convergence in applications related to Bayesian experimental design and uncertainty quantification.
Blanchard, Antoine, Sapsis, Themistoklis
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GENERATIONS IN BAYESIAN NETWORKS
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019 This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko +3 more
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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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