Nuclear Computations under Uncertainty New methods to infer and propagate nuclear data uncertainty across Monte Carlo simulations
This thesis introduces new methods to efficiently infer and propagate nuclear data uncertainty across Monte Carlo simulations of nuclear technologies. The main contributions come in two areas: 1. novel statistical methods and machine learning algorithms (Embedded Monte Carlo); 2. new mathematical p...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139530 https://orcid.org/0000-0001-8146-4648 |