Report from the A.I. For Nuclear Physics Workshop
arXiv:2006.05422
Abstract
This report is an outcome of the workshop "AI for Nuclear Physics" held at Thomas Jefferson National Accelerator Facility on March 4-6, 2020. The workshop brought together 184 scientists to explore opportunities for Nuclear Physics in the area of Artificial Intelligence. The workshop consisted of plenary talks, as well as six working groups. The report includes the workshop deliberations and additional contributions to describe prospects for using AI across Nuclear Physics research.
This version includes reference updates, improved figures and minor clarifications in the text
References in corpus (16)
- Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
- Constraining the Eq. of State of Super-Hadronic Matter from Heavy-Ion Collisions
- A data-driven analysis for the temperature and momentum dependence of the heavy quark diffusion coefficient in relativistic heavy-ion collisions
- Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements
- Global sensitivity analysis of bulk properties of an atomic nucleus
- Neural Networks for Modeling and Control of Particle Accelerators
- Direct comparison between Bayesian and frequentist uncertainty quantification for nuclear reactions
- Principal component analysis of event-by-event fluctuations
- Background rejection in NEXT using deep neural networks
- Beyond the proton drip line: Bayesian analysis of proton-emitting nuclei
- Quantifying uncertainties in neutron-alpha scattering with chiral nucleon-nucleon and three-nucleon forces
- Interpretable deep learning for nuclear deformation in heavy ion collisions
- Improvement Studies of an Effective Interaction for N=Z sd-shell Nuclei by Neural Networks
- Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
- Pairing correlations and eigenvalues of two-body density matrix in atomic nuclei
- Ab initio models of atomic nuclei: challenges and new ideas