Results 21 to 30 of about 1,665,658 (262)

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

To the brave scientists: Aren't we strong enough to stand (and profit from) uncertainty in Earth system measurement and modelling?

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, Matthias Gross, Jakob Lüttgau, David S. Greenberg, Tobias Weigel   +4 more
doaj   +1 more source

Multi-Fidelity Low-Rank Approximations for Uncertainty Quantification of a Supersonic Aircraft Design

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, Hayriye Pehlivan Solak, Melike Nikbay   +2 more
doaj   +1 more source

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
doaj   +3 more sources

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, Yuhang Su, Jie Wang, Chengcheng Deng, Qingyu Huang, Shuang Zhang, Bin Wu, Yuanfeng Lin   +7 more
doaj   +1 more source

Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification

, 2021
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, Antoine Blanchard, Themistoklis Sapsis   +3 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, Michael Auinger, Peter J. Thomas, Marks Legkovskis   +5 more
core   +1 more source

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, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukas Novak, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner   +11 more
doaj   +1 more source

Optimal Power Procurement for Green Cellular Wireless Networks Under Uncertainty and Chance Constraints

Entropy
Given 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, Shyam Mohan Subbiah Pillai, Raúl Tempone   +2 more
doaj   +1 more source

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, Alexander Mitsos, Raúl Tempone, Manuel Dahmen   +3 more
doaj   +1 more source