<i>Colloquium</i>: Machine learning in nuclear physics
202 Citations•2022•
A. Boehnlein, Markus Diefenthaler, N. Sato
This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.
Abstract
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Colloquium provides a snapshot of nuclear physics research, which has been transformed by machine learning techniques.