Learning Nuclear Structure with AI: Radii and Collectivity
nucl-th, cs.AI, cs.LG, nucl-ex
Submitted: 2026-09-15
Updated: 2026-09-15
Comments: 15 pages, 7 figures, 2 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Coupled-cluster computations of atomic nuclei
- The In-Medium Similarity Renormalization Group: A Novel Ab Initio Method for Nuclei
- Multi-reference many-body perturbation theory for nuclei I -- Novel PGCM-PT formalism
- Lattice Effective Field Theory Simulations of Nuclei
- A Guided Tour of Ab Initio Nuclear Many-Body Theory
- What is ab initio in nuclear theory?
- Modern Theory of Nuclear Forces
- Chiral effective field theory and nuclear forces
- Nuclear effective field theory: status and perspectives
- Eigenvector continuation with subspace learning
- Eigenvector Continuation and Projection-Based Emulators
- Parametric Matrix Models
- An Efficient Learning Method to Connect Observables
- Global sensitivity analysis of bulk properties of an atomic nucleus
- Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables
- Ab initio predictions link the neutron skin of ${}^{208}$Pb to nuclear forces
- Ab initio uncertainty quantification of neutrinoless double-beta decay in $^{76}$Ge
- Multiscale physics of atomic nuclei from first principles
- Global Framework for Emulation of Nuclear Calculations
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