Alert button
Picture for Amber Boehnlein

Amber Boehnlein

Alert button

Artificial Intelligence and Machine Learning in Nuclear Physics

Dec 04, 2021
Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler

Figure 1 for Artificial Intelligence and Machine Learning in Nuclear Physics
Figure 2 for Artificial Intelligence and Machine Learning in Nuclear Physics
Figure 3 for Artificial Intelligence and Machine Learning in Nuclear Physics
Figure 4 for Artificial Intelligence and Machine Learning in Nuclear Physics

Advances in artificial intelligence/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 Review gives a snapshot of nuclear physics research which has been transformed by artificial intelligence and machine learning techniques.

* Comments are welcome 
Viaarxiv icon