Results 31 to 40 of about 252,868 (269)
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
, 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.
Huang, Ziyi, Lam, Henry, Zhang, Haofeng
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Calculation of Generalized Polynomial-Chaos Basis Functions and Gauss Quadrature Rules in Hierarchical Uncertainty Quantification [PDF]
, 2014 Stochastic spectral methods are efficient techniques for uncertainty quantification. Recently they have shown excellent performance in the statistical analysis of integrated circuits.
Daniel, Luca +4 more
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Multilevel Quasi-Monte Carlo Methods for Lognormal Diffusion Problems [PDF]
, 2016 In this paper we present a rigorous cost and error analysis of a multilevel estimator based on randomly shifted Quasi-Monte Carlo (QMC) lattice rules for lognormal diffusion problems. These problems are motivated by uncertainty quantification problems in
Kuo, Frances Y. +4 more
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The value of carbon sequestration and storage in coastal habitats [PDF]
, 2014 Coastal margin habitats are globally significant in terms of their capacity to sequester and store carbon, but their continuing decline, due to environmental change and human land use decisions, is reducing their capacity to provide this ecosystem ...
A. Garbutt +67 more
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Quantification and Reduction of Uncertainties in 3D Stress Models
, 2020 <p>The undisturbed stress state of a potential site for nuclear waste disposal is of key importance for the assessment of long-term stability of the geotechnical installations and for seismic hazard assessment. In particular, the stability of pre-existing faults within and near a repository can only be evaluated with the ...
Moritz Ziegler, Oliver Heidbach
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Case Studies in Thermal EngineeringThis study numerically investigates the combined effects of double flat aerodisks and rear opposing jets on the drag and heat reduction performance of a hypersonic spiked blunt body. Uncertainty quantification and sensitivity analysis are performed using
Ni He, Yu Pan, Zhenkang Zhang, Jian Chen
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An Efficient Polynomial Chaos Expansion Method for Uncertainty Quantification in Dynamic Systems
Applied Mechanics, 2021 Uncertainty is a common feature in first-principles models that are widely used in various engineering problems. Uncertainty quantification (UQ) has become an essential procedure to improve the accuracy and reliability of model predictions.
Jeongeun Son, Yuncheng Du
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Uncertainty Quantification and Sensitivity Analysis of Nuclear Construction Cost Reduction Pathways
EnergiesHigh construction costs have plagued recent nuclear projects and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a plant economic framework that quantifies the impact that various plant design and ...
Rowan Marchie +6 more
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Coupling the reduced-order model and the generative model for an importance sampling estimator
, 2019 In this work, we develop an importance sampling estimator by coupling the reduced-order model and the generative model in a problem setting of uncertainty quantification.
Wan, Xiaoliang, Wei, Shuangqing
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