325 citations · 1.7k across the 32 of their papers we have counts for
6 papers · 1 filter
Active learning emulators for nuclear two-body scattering in momentum space
A. Giri, J. Kim, C. Drischler +2
We extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to cou…
Emulation of Proton-Deuteron Scattering via the Reduced Basis Method and Active Learning: Detailed Description
Alex Gnech, Xilin Zhang, Christian Drischler +5
Nucleon-deuteron () scattering can be used to constrain three-nucleon forces in chiral effective field theory (EFT). However, high-fidelity calculations, such as the Hypersp…
Accurate and Efficient Emulation of Proton-Deuteron Scattering via the Reduced Basis Method and Active Learning
Alex Gnech, Xilin Zhang, Christian Drischler +5
We introduce highly accurate and efficient emulators for proton-deuteron scattering below the deuteron breakup threshold. We explore two different reduced-basis method strategies:…
The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions
Jérôme Margueron, Christian Drischler, Mariana Dutra +10
Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are perfo…
Microscopic constraints for the equation of state and structure of neutron stars: a Bayesian model mixing framework
A. C. Semposki, C. Drischler, R. J. Furnstahl +1
Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework f…
Greedy Emulators for Nuclear Two-Body Scattering
J. M. Maldonado, C. Drischler, R. J. Furnstahl +1
Applications of reduced basis method emulators are increasing in low-energy nuclear physics because they enable fast and accurate sampling of high-fidelity calculations, enabling r…