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
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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 +7 more
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IEEE Transactions on Industrial Informatics, 2023 The automatic and accurate analysis of medical images (e.g., segmentation,detection, classification) are prerequisites for modern disease diagnosis and prognosis.
Moloud Abdar +9 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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Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [PDF]
Transactions of the Association for Computational LinguisticsThe rapid proliferation of large language models (LLMs) has stimulated researchers to seek effective and efficient approaches to deal with LLM hallucinations and low-quality outputs.
Roman Vashurin +13 more
semanticscholar +1 more source
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification [PDF]
European Conference on Computer Vision, 2022 A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geometry of the scene.
Jianxiong Shen +3 more
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Uncertainty quantification for Bayesian CART [PDF]
The Annals of Statistics, 2021 This work affords new insights into Bayesian CART in the context of structured wavelet shrinkage. The main thrust is to develop a formal inferential framework for Bayesian tree-based regression. We reframe Bayesian CART as a g-type prior which departs from the typical wavelet product priors by harnessing correlation induced by the tree topology.
Castillo, Ismaël, Ročková, Veronika
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SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification [PDF]
IEEE Transactions on Biomedical Engineering, 2021 Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model,
Huy P Phan +5 more
semanticscholar +1 more source
Deep evidential learning in diffusion convolutional recurrent neural network
Electronic Research Archive, 2023 Graph neural networks (GNNs) is applied successfully in many graph tasks, but there still exists a limitation that many of GNNs model do not consider uncertainty quantification of its output predictions.
Zhiyuan Feng +5 more
doaj +1 more source
Uncertainty Quantification of Future Design Rainfall Depths in Korea [PDF]
, 2019 One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios.
Handmer +6 more
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