Results 11 to 20 of about 1,665,658 (262)

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., Pyle, Richard J., Ali, Amine Ait Si, Hughes, Robert R.   +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, Johannes, Kircher, Jonny, Proppe, Proppe, Jonny, Kircher, Johannes; 1 Georg-August University Institute of Physical Chemistry Tammannstrasse 6 37077 Göttingen Germany, Proppe, Jonny; 1 Georg-August University Institute of Physical Chemistry Tammannstrasse 6 37077 Göttingen Germany, Jonny Proppe, Johannes Kircher   +7 more
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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, Wei, Xinpeng, Du, Xiaoping, He, Xiaoming, Han, Daoru, Zhao, Jianxun, Xiaoping Du, Daoru Han, Xiaoming He, Xinpeng Wei, Jianxun Zhao, Zhen Hu   +11 more
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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
core   +1 more source

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

Aleatory-aware deep uncertainty quantification for transfer learning

, 2022
The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty.
Mondal, Subrota Kumar, Nahavandi, Saeid, Acharya, U. Rajendra, Khozeimeh, Fahime, Khanam, Sadia, Khosravi, Abbas, Kabir, H M Dipu   +6 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, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone   +3 more
doaj   +1 more source

Multidimensional integration using machine learning and Monte Carlo methods for acoustic predictions [PDF]

ESAIM: Proceedings and Surveys
To 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, Laoye Victoria Eshomomoh, Mannes Yolhan, Mohan Shyam, Pillai Subbiah, Trafny Nicolas   +5 more
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Analysis, Quantification and Evaluation of Uncertainty

, 2022
S.113-207This chapter describes the various approaches to analyse, quantify and evaluate uncertainty along the phases of the product life cycle. It is based on the previous chapters that introduce a consistent classification of uncertainty and a holistic
Platz, R., Brötz, J., Weigold, M., Dietrich, I., Feldmann, R., Rexer, M., Schaeffner, M., Gehb, C.M., Hartig, J., Matei, A., Geßner, F., Lenz, J., Martin, D., Kohler, M., Ulbrich, S., Öztürk, T., Pfetsch, M.E., Bölling, C., Hoppe, F., Hedrich, P., Abele, E., Melz, T., Weber, M., Pelz, P.F., Kersting, S., Staudter, G., Anderl, R.   +26 more
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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, Natalya Litvinenko, Orken Mamyrbayev, Assem Shayakhmetova   +3 more
doaj   +1 more source