Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions.
Abstract not provided.
Robbe, Pieterjan↗
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Results for “uncertainty Quantification”
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Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions.
Abstract not provided.
Robbe, Pieterjan↗
Multifidelity Uncertainty Quantification For Non-Deterministic Models.
Abstract not provided.
Reuter, Bryan↗
Uncertainty Quantification and Active Learning in Atomistic Computations.
Abstract not provided.
Najm, Habib↗
Discussion: Research Advances at the Interface of Uncertainty Quantification and Machine Learning for High Consequence Problems.
Abstract not provided.
Ries, Daniel↗
Estimating Seismic Source Time Functions using Time Domain Inversion with Uncertainty Quantification.
Abstract not provided.
Wells, Daniel↗
Uncertainty Quantification of Machine Learning Interatomic Potential Models.
Abstract not provided.
Sargsyan, Khachik↗
Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method.
Abstract not provided.
Tran, Anh↗
Multimodel Methods for Uncertainty Quantification of Repository Systems.
Abstract not provided.
Portone, Teresa↗
Multifidelity Uncertainty Quantification in Stochastic Media Transport Problems.
Abstract not provided.
Geraci, Gianluca↗
Uncertainty Quantification of Fuel Inhomogeneity in Low-enriched Uranium Silicide High Flux Isotope Reactor Design
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11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Improving Density Curve Predictions and Uncertainty Quantification with Functional Data Analysis [Slides]
Los Alamos National Laboratory (LANL) has developed advanced simulation methods for predicting subsurface flow.
97 MATHEMATICS AND COMPUTING↗
Practical VVUQ–The Interplay of Model Form, Uncertainty Quantification and Error in Computational Predictions from a Validation Assessment Perspective [Slides]
Abstract not provided.
97 MATHEMATICS AND COMPUTING↗
A Polynomial Chaos Approach for Uncertainty Quantification of Monte Carlo Transport Codes
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Geraci, Gianluca↗
Ice Sheet Models of Different Fidelity for Uncertainty Quantification
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Jakeman, John Davis↗
The Approximate Control Variate Framework for Efficient Multifidelity Uncertainty Quantification of Non-Deterministic Models
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Wildey, Timothy Michael↗
Uncertainty quantification for quantitative validation of laser powder bed fusion process outcomes
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Moser, Daniel Robert↗