Results 51 to 60 of about 3,213,103 (353)

A distributionally robust perspective on uncertainty quantification and chance constrained programming [PDF]

, 2015
The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability.
Hanasusanto, GA, Kuhn, D, Roitch, V, Wiesemann, W   +3 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

An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change [PDF]

Earth System Dynamics
This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario ...
J. D. Jakeman, M. Perego, D. T. Seidl, T. A. Hartland, T. R. Hillebrand, M. J. Hoffman, S. F. Price   +6 more
doaj   +1 more source

Complex model calibration through emulation, a worked example for a stochastic epidemic model

Epidemics, 2022
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology,
Michael Dunne, Hossein Mohammadi, Peter Challenor, Rita Borgo, Thibaud Porphyre, Ian Vernon, Elif E. Firat, Cagatay Turkay, Thomas Torsney-Weir, Michael Goldstein, Richard Reeve, Hui Fang, Ben Swallow   +12 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

Uncertainty quantification by direct propagation of shallow ensembles [PDF]

Machine Learning: Science and Technology
Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or ...
Matthias Kellner, Michele Ceriotti
semanticscholar   +1 more source

Multifidelity Uncertainty Propagation via Adaptive Surrogates in Coupled Multidisciplinary Systems [PDF]

, 2018
Fixed point iteration is a common strategy to handle interdisciplinary coupling within a feedback-coupled multidisciplinary analysis. For each coupled analysis, this requires a large number of disciplinary high-fidelity simulations to resolve the ...
Chaudhuri, Anirban, Lam, Remi, Willcox, Karen E   +2 more
core   +1 more source

Democratizing uncertainty quantification

Journal of Computational Physics
Add Benjamin Kent as co-author in accordance with the paper's published ...
Seelinger L., Reinarz A., Lykkegaard M. B., Akers R., Alghamdi A. M. A., Aristoff D., Bangerth W., Bénézech J., Diez M., Frey K., Jakeman J. D., Jørgensen J. S., Kim K. T., Kent B. M., Martinelli M., Parno M., Pellegrini R., Petra N., Riis N. A. B., Rosenfeld K., Serani A., Tamellini L., Villa U., Dodwell T. J., Scheichl R.   +24 more
openaire   +6 more sources

Uncertainty quantification in breakup reactions

Physical Review C, 2022
Breakup reactions are one of the favored probes to study loosely bound nuclei, particularly those in the limit of stability forming a halo. In order to interpret such breakup experiments, the continuum discretized coupled channel method is typically used. In this study, the first Bayesian analysis of a breakup reaction model is performed.
Ö. Sürer, F. M. Nunes, M. Plumlee, S. M. Wild   +3 more
openaire   +4 more sources