509 citations · 3.2k across the 61 of their papers we have counts for
5 papers · 1 filter
Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics
D. R. Phillips, R. J. Furnstahl, U. Heinz +8
We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental…
Machine learning-based inversion of nuclear responses
Krishnan Raghavan, Prasanna Balaprakash, Alessandro Lovato +2
A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the corre…
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
Raghu Bollapragada, Matt Menickelly, Witold Nazarewicz +3
We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of par…
Calibration of Energy Density Functionals with Deformed Nuclei
N. Schunck, J. O'Neal, M. Grosskopf +2
Nuclear density functional theory is the prevalent theoretical framework for accurately describing nuclear properties at the scale of the entire chart of nuclides. Given an energy…
Tuning Multigrid Methods with Robust Optimization
Jed Brown, Yunhui He, Scott MacLachlan +2
Local Fourier analysis is a useful tool for predicting and analyzing the performance of many efficient algorithms for the solution of discretized PDEs, such as multigrid and domain…