SECURE: Science and Engineering of Cyber security by Uncertainty quantification and Rigorous Experimentation .
Abstract not provided.
Pinar, Ali↗
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Results for “Uncertainty quantification”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Sparse Data Machine Learning Integration with Theory, Experiment and Uncertainty Quantification: Process-Structure-Property-Performance of Friction Deformation Processing
Computer vision and deep learning tools that advance the ability to establish processing-structure-property-performance (PSPP) relations are presented. The Bayesian binning method for image segmentation enables quantitative analysis of microstructural features in an automated way, while the analysis of shapes and relative orientation of these features reveals local deformation maps indicative of both, material flow and residual stresses due to materials processing. The deep learning method leads to the previous knowledge agnostic mapping of empirically observed microstructural zones in friction stir welding (FSW) process and synthetic microstructure generation capability that is statistically equivalent to experimentally collected data.
97 MATHEMATICS AND COMPUTING↗
An Uncertainty Quantification Enabled Semi-Supervised Paired Neural Network for Few-Shot Classification.
Abstract not provided.
Ries, Daniel↗
Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models.
Abstract not provided.
Reuter, Bryan↗
The UQTk C++/Python Toolkit for Uncertainty Quantification: Overview and Applications.
Abstract not provided.
Boll, Luke↗
Posterior Predictive Variational Inference for Uncertainty Quantification in Machine Learning.
Abstract not provided.
Catanach, Thomas↗
Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions.
Abstract not provided.
Robbe, Pieterjan↗
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↗