Journal
JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
Volume 42, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/0954-3899/42/3/034024
Keywords
nuclear structure; uncertainty quantification; density functional theory; high performance computing; energy functional; Bayesian statistics; covariance
Categories
Funding
- US Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
- SciDAC activity within the US Department of Energy, Office of Science, Advanced Scientific Computing Research [DE-AC02-06CH11357]
- Livermore Computing Resource Center at Lawrence Livermore National Laboratory
- Laboratory Computing Resource Center at Argonne National Laboratory
- National Center for Computational Sciences (NCCS)
- National Institute for Computational Sciences (NICS) at Oak Ridge National Laboratory
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Nuclear density functional theory (DFT) is the only microscopic, global approach to the structure of atomic nuclei. It is used in numerous applications, from determining the limits of stability to gaining a deep understanding of the formation of elements in the Universe or the mechanisms that power stars and reactors. The predictive power of the theory depends on the amount of physics embedded in the energy density functional as well as on efficient ways to determine a small number of free parameters and solve the DFT equations. In this article, we discuss the various sources of uncertainties and errors encountered in DFT and possible methods to quantify these uncertainties in a rigorous manner.
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