45 citations · 87 across the 4 of their papers we have counts for
4 papers
MOOSE ProbML: Parallelized Probabilistic Machine Learning and Uncertainty Quantification for Computational Energy Applications
Somayajulu L. N. Dhulipala, Peter German, Yifeng Che +5
This paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphy…
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
Somayajulu L. N. Dhulipala, Michael D. Shields, Promit Chakroborty +7
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However,…
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation
S. L. N. Dhulipala, M. D. Shields, B. W. Spencer +4
While multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by a…
Implicit Filtered PN for High-Energy Density Thermal Radiation Transport using Discontinuous Galerkin Finite Elements
Vincent M. Laboure, Ryan G. McClarren, Cory D. Hauck
In this work, we provide a fully-implicit implementation of the time-dependent, filtered spherical harmonics (FPN) equations for non-linear, thermal radiative transfer. We investig…