Results 31 to 40 of about 44,636 (300)
AIP Advances, 2023 The determination of rate coefficient parameters in detailed chemical kinetic mechanisms through experiments often suffers from avoidable aleatory uncertainty, while the use of reduced mechanisms, based on various reduction methods, introduces epistemic ...
Linying Li, Bin Zhang, Hong Liu
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, 2022 We consider the use of Gaussian process (GP) priors for solving inverse problems in a Bayesian framework. As is well known, the computational complexity of GPs scales cubically in the number of datapoints.
Linde, Niklas +5 more
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Energies, 2019 This work presents the Third-Order Adjoint Sensitivity Analysis Methodology (3rd-ASAM) for response-coupled forward and adjoint linear systems.
Dan Gabriel Cacuci
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Functional error modeling for uncertainty quantification in hydrogeology [PDF]
, 2015 Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation.
Josset, L. +6 more
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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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, 2017 Component-based system simulation models are used throughout all development phases for design and verification of both physical systems and control software, not least in the aeronautical industry.
Eek, Magnus, +2 more
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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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Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems [PDF]
International Journal for Numerical Methods in Engineering, 2009 AbstractWe present a model reduction approach to the solution of large‐scale statistical inverse problems in a Bayesian inference setting. A key to the model reduction is an efficient representation of the non‐linear terms in the reduced model. To achieve this, we present a formulation that employs masked projection of the discrete equations; that is ...
Galbally, David +3 more
openaire +4 more sources
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
wiley +1 more source