509 citations · 3.2k across the 61 of their papers we have counts for
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Nuclear Energy Density Optimization: UNEDF2
M. Kortelainen, J. McDonnell, W. Nazarewicz +8
The parameters of the UNEDF2 nuclear energy density functional (EDF) model were obtained in an optimization to experimental data consisting of nuclear binding energies, proton radi…
Error Analysis in Nuclear Density Functional Theory
Nicolas Schunck, Jordan D. McDonnell, Jason Sarich +2
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…
A Bayesian Approach for Parameter Estimation and Prediction using a Computationally Intensive Model
Dave Higdon, Jordan D. McDonnell, Nicolas Schunck +2
Bayesian methods have been very successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are…
Statistical uncertainties of a chiral interaction at next-to-next-to leading order
A. Ekström, B. D. Carlsson, K. A. Wendt +4
We have quantified the statistical uncertainties of the low-energy coupling-constants (LECs) of an optimized nucleon-nucleon (NN) interaction from chiral effective field theory ($χ…
Derivative-free optimization for parameter estimation in computational nuclear physics
Stefan M. Wild, Jason Sarich, Nicolas Schunck
We consider optimization problems that arise when estimating a set of unknown parameters from experimental data, particularly in the context of nuclear density functional theory. W…
Quantification of Uncertainties in Nuclear Density Functional theory
N. Schunck, J. D. McDonnell, D. Higdon +2
Reliable predictions of nuclear properties are needed as much to answer fundamental science questions as in applications such as reactor physics or data evaluation. Nuclear density…