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 +12 more
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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 +3 more
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A decomposition-based uncertainty quantification approach for environmental impacts of aviation technology and operation [PDF]
, 2018 As a measure to manage the climate impact of aviation, significant enhancements to aviation technologies and operations are necessary. When assessing these enhancements and their respective impacts on the climate, it is important that we also quantify ...
Allaire, Douglas L +3 more
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Uncertainty quantification by direct propagation of shallow ensembles [PDF]
Machine Learning: Science and TechnologyStatistical 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
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures
Entropy, 2021 Uncertainty quantification for complex deep learning models is increasingly important as these techniques see growing use in high-stakes, real-world settings.
Benjamin Kompa +2 more
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IEEE transactions on industrial electronics (1982. Print), 2021 Predicting future capacities and remaining useful life (RUL) with uncertainty quantification is a key but challenging issue in the applications of battery health diagnosis and management.
Kailong Liu +3 more
semanticscholar +1 more source
An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change [PDF]
Earth System DynamicsThis 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 +6 more
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Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) [PDF]
Geoscientific Model Development, 2020 Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data.
Z. Yin, S. Strebelle, J. Caers
doaj +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 +2 more
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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 +3 more
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