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Improved Uncertainty Quantification with Advanced Reactor Application
This document provides an overview of the economic and technical challenges related to bringing small modular reactors to market and then presents an outline for how to address the new challenges. The purpose of this project was to proactively design software for its intended use to provide a strategic positioning for work in the future. This project seeks to augment the short-term stop-gap approach of trying to use legacy software well outside of its range of applicability.
Active Learning and Uncertainty Quantification for Machine Learning Interatomic Potentials.
Abstract not provided.
Uncertainty Quantification and Sensitivity Analysis for Simulation of Hostile Blast Events [Slides]
A mesh convergence study shows that shock arrival times converge for basic flow shots. Sensitivity study reveals a surprisingly high sensitivity to the placement of the HE package. Bayesian inference tools used on the experimental datasets suggest this is a real effect. Sensitivity analysis of ambient conditions such as temperature and pressure reveal minimal effect. Porting of pressure data from QUINOA simulations to perform structural analysis on the aeroshell geometry was successful, and shows a strong dependence of payload response to the angle of attack.
SECURE: Science and Engineering of Cyber security by Uncertainty quantification and Rigorous Experimentation .
Abstract not provided.
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.
An Uncertainty Quantification Enabled Semi-Supervised Paired Neural Network for Few-Shot Classification.
Abstract not provided.
Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models.
Abstract not provided.
The UQTk C++/Python Toolkit for Uncertainty Quantification: Overview and Applications.
Abstract not provided.
Posterior Predictive Variational Inference for Uncertainty Quantification in Machine Learning.
Abstract not provided.
Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions.
Abstract not provided.
Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions.
Abstract not provided.
Multifidelity Uncertainty Quantification For Non-Deterministic Models.
Abstract not provided.
Uncertainty Quantification and Active Learning in Atomistic Computations.
Abstract not provided.
Discussion: Research Advances at the Interface of Uncertainty Quantification and Machine Learning for High Consequence Problems.
Abstract not provided.
Estimating Seismic Source Time Functions using Time Domain Inversion with Uncertainty Quantification.
Abstract not provided.
Uncertainty Quantification of Machine Learning Interatomic Potential Models.
Abstract not provided.