Results 21 to 30 of about 110,431 (303)

Uncertainty Quantification for Deep Learning in Ultrasonic Crack Characterization [PDF]

, 2022
Deep learning for Non-Destructive Evaluation (NDE) has received a lot of attention in recent years for its potential ability to provide human level data analysis. However, little research into quantifying the uncertainty of its predictions has been done.
Wilcox, Paul D., Ait Si Ali, Amine, Wilcox, Paul, Pyle, Richard, Pyle, Richard J., Ali, Amine Ait Si, Wilcox, Paul D, Hughes, Robert R, Hughes, Robert, Hughes, Robert R.   +9 more
core   +1 more source

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

Democratizing uncertainty quantification [PDF]

Journal of Computational Physics
Add Benjamin Kent as co-author in accordance with the paper's published ...
Linus Seelinger, Anne Reinarz, Mikkel Bue Lykkegaard, Robert Akers, Amal Mohammed A. Alghamdi, David Aristoff, Wolfgang Bangerth, Jean Bénézech, Matteo Diez, Kurt Frey, John D. Jakeman, Jakob Sauer Jørgensen, Ki-Tae Kim, Benjamin M. Kent, Massimiliano Martinelli, Matthew D. Parno, Riccardo Pellegrini, Noemi Petra, Nicolai André Brogaard Riis, Katherine Rosenfeld, Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl   +24 more
openaire   +5 more sources

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
core   +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
doaj   +1 more source

Uncertainty in Engineering [PDF]

, 2022
This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory

core   +1 more source

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

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
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
core   +1 more source

Uncertainty quantification of time-dependent quantities in a system with adjustable level of smoothness [PDF]

, 2021
We summarise the results of a computational study involved with Uncertainty Quantification (UQ) in a benchmark turbulent burner flame simulation. UQ analysis of this simulation enables one to analyse the convergence performance of one of the most widely ...
Thomas, Peter J., Legkovskis, Marks, Auinger, Michael   +2 more
core   +1 more source