activity
19952026
most citedImproved nuclear matter calculations from chiral low-momentum interactions

587 citations · 3.9k across the 72 of their papers we have counts for

collaborators
Showing 2025 · nucl-thShow all

9 papers · 2 filters

nucl-th2025

Wavefunction-Based Emulation of Coupled-Channels Scattering with Non-Affinely Parametrized Interactions

M. Catacora-Rios, Kyle Beyer, Pablo Giuliani +3

Physics based emulators offer a fast and reliable replacement for an exact solution of the scattering problem in nuclear physics. Previous work developed a reduced-basis emulator f…

nucl-th2025★ 1 cited

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…

nucl-th2025

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…

nucl-th2025

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:…

nucl-th2025

Assessing Convergence Patterns Across Modern Nucleon-Nucleon Potentials

P. J. Millican, R. J. Furnstahl, J. A. Melendez +1

The BUQEYE model for correlated effective field theory (EFT) truncation errors assumes a regular pattern of dimensionless coefficients extracted from order-by-order observable calc…

nucl-th2025

Criticality analysis of nuclear binding energy neural networks

S. A. Sundberg, R. J. Furnstahl

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics…