Overview

Over the last decade there has been significant development in machine learning and artificial intelligence, with supervised and unsupervised computational learning tools now used routinely in scientific applications. Building on this progress, the focus of this program is on the use and future impacts of machine learning in nuclear theory, bringing together researchers with focuses in lattice QCD and statistical systems, hadron and nuclear structure, many-body theory, quantum computing, nuclear astrophysics, and hot and dense matter, to explore common interests in machine learning tools and applications.

The week-long embedded workshop is targeted at the exploration of connections to researchers and ideas from related fields including those outside nuclear theory and in the connection to experimental programs such as Jefferson Lab, RHIC, FRIB and the future EIC, as well as discussion of common themes in scientific machine learning including uncertainty quantification, robustness, and the inclusion of physical constraints, and at discussion of the progress required for machine learning for nuclear theory to best utilise state of the art computational resources and exascale computing capabilities.

Program Format

Week 1 - Embedded Workshop

Week 2 - Program

Week 3 - Program

Week 4 - Program