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Center for Nonlinear Studies |
While DOE has moved to a scientific paradigm driven by modeling and simulation – and in which experimentation is motivated primarily by code validation – there is still much to be learned by analyzing data directly and extracting information from experimental data by solving inverse problems. In order to quantify the uncertainties associated with the solutions, however, it is necessary to use statistical approaches to formulating the inverse problems and to understand the nature of the uncertainties for which such formulations can correctly account. In this work we will present data from NNSA X-ray imaging experiments related to the stockpile stewardship program, some inverse problems whose solutions inform the evolution of our experiments and diagnostics systems, and the challenges associated with the Bayesian formalisms used to assign error bars to the information extracted. The discussion will include details of the experiments themselves, where mathematical data analysts fit into the experimental programs, the role of mathematical theory in development of analysis techniques, and results demonstrating the efficacy of solving statistical inverse problems to drive stockpile stewardship. Collaborators include Aaron Luttman (NSTec), Michael Fowler (MathWorks), Earl Lawrence (LANL), Kevin Joyce (University of Montana), Maggie Hock (University of Alabama Huntsville), Jesse Adams (University of Arizona), Johnathan Bardsley (University of Montana), Stephen Mitchell (NSTec), and Eric Machorro (NSTec). This work was done by National Security Technologies, LLC, under Contract No. DE-AC52-06NA25946 with the U.S. Department of Energy and supported by the Site-Directed Research and Development Program.