Modeling material variability with uncertainty quantification and machine learning techniques
Abstract not provided.
Publications
Modeling material variability with uncertainty quantification and machine learning techniques
Abstract not provided.
Solution of the Generalized Linear Boltzmann Equation for Multidimensional Stochastic Media
The generalized linear Boltzmann equation is a recently developed framework based on non-classical transport theory for modeling the expected value of particle flux in an arbitrary stochastic medium. Provided with a non-classical cross-section for a given statistical description of a medium, any transport problem in that medium may be solved. Previous work has only considered one-dimensional media without finite boundary conditions and discrete binary mixtures of materials. In this work the solution approach for the GLBE in multidimensional media with finite boundaries is outlined. The discrete ordinates method with an implicit discretization of the pathlength variable is used to leverage sweeping methods for the transport operator. In addition, several convenient approximations for non-classical cross-sections are introduced. The solution approach is verified against random realizations of a Gaussian process medium in a square enclosure.
Multi-level Uncertainty Quantification of a Wind Turbine Large Eddy Simulation Model
Abstract not provided.
Embedded Model Error and Bayesian Model Selection for Material Variability
Abstract not provided.
Modeling material variability with uncertainty quantification and machine learning techniques
Abstract not provided.
Multifidelity Uncertainty Quantification for Radiation Transport
Abstract not provided.
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