Results 11 to 20 of about 44,636 (300)
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Advances in Neural Information Processing Systems 36, 2023 Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models. In deep learning, uncertainties arise not only from data, but also from the training procedure that often injects substantial noises and biases.
Ziyi Huang +2 more
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Ensemble-based uncertainty quantification and reduction in hydrological modelling and predictions
, 2019 In the contemporary world, many environmental and water resources related decisions rely upon wide range of modelling results. However, models are simplified representations of reality and their predictions are generally imperfect due to the inherent uncertainties emanating from various sources.
Teweldebrhan, Aynom Tesfay
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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 +4 more
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Quantification and Reduction of Uncertainty in Seismic Resilience Assessment for a Roadway Network
Infrastructures, 2023 The nation’s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic ...
Vishnupriya Jonnalagadda +3 more
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Quantifying location error to define uncertainty in volcanic mass flow hazard simulations [PDF]
Natural Hazards and Earth System Sciences, 2021 The use of mass flow simulations in volcanic hazard zonation and mapping is often limited by model complexity (i.e. uncertainty in correct values of model parameters), a lack of model uncertainty quantification, and limited approaches to incorporate this
S. R. Mead, J. Procter, G. Kereszturi
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IEEE Access, 2023 In electrical impedance tomography (EIT), the uncertainty of conductivity distribution may cause the uncertainty in the forward calculation and further affect the inverse problem.
Yingge Zhao +4 more
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Bayesian Quantification and Reduction of Uncertainties in 3D Geomechanical‐Numerical Models
Journal of Geophysical Research: Solid Earth, 2022 AbstractThe distance to failure of the upper crustal rock in the prevalent stress field is of importance to better understand fault reactivation by natural and induced processes as well as to plan and manage georeservoirs. In particular, the contemporary stress state is one of the key ingredients for this assessment. To provide a continuous description
Ziegler, Moritz O. +2 more
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, 2022 S.241-256Different mathematical models can be developed to represent the dynamic behavior of structural systems and assess properties, such as risk of failure and reliability. Selecting an adequate model requires choosing a model of sufficient complexity
Platz, Roland +4 more
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Energies, 2022 The computational models of physical systems comprise parameters, independent and dependent variables [...]
Dan Gabriel Cacuci
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DATA ASSIMILATION APPLIED TO PRESSURISED WATER REACTORS [PDF]
EPJ Web of Conferences, 2021 Best estimate plus uncertainty is the leading methodology to validate existing safety margins. It remains a challenge to develop and license these approaches, in part due to the high dimensionality of system codes. Uncertainty quantification is an active
Dixon J.R +3 more
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