https://doi.org/10.1140/epja/s10050-020-00290-x
Review
A.I. for nuclear physics
1
University of Maryland, College Park, MD, USA
2
Thomas Jefferson National Accelerator Facility, Newport News, VA, USA
3
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
4
Catholic University, Washington, D.C., USA
5
Davidson College, Davidson, NC, USA
6
Michigan State University, East Lansing, MI, USA
7
College of William & Mary, Williamsburg, VA, USA
8
Oak Ridge National Laboratory, Oak Ridge, TN, USA
9
Pacific Northwest National Laboratory, Richland, WA, USA
b
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Received:
9
September
2020
Accepted:
7
October
2020
Published online:
22
March
2021
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
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.
Disclaimer: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177. Participation of students and early career professionals was supported by NSF, Division of Physics, under the Grant ‘Artificial Intelligence (AI) Workshop in Nuclear Physics,’ Award Number 2017170. Support for the Hackathon was provided by the University of Virginia School of Data Sciences and by Amazon Web Services.
© Jefferson Science Associates, LLC, under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021
