Machine Learning in Nuclear Physics
Boehnlein, Amber, Diefenthaler, Markus, Fanelli, Cristiano, Hjorth-Jensen, Morten, Horn, Tanja, Kuchera, Michelle P., Lee, Dean, Nazarewicz, Witold, Orginos, Kostas, Ostroumov, Peter, Pang, Long-Gang, Poon, Alan, Sato, Nobuo, Schram, Malachi, Scheinker, Alexander, Smith, Michael S., Wang, Xin-Nian, Ziegler, Veronique
–arXiv.org Artificial Intelligence
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 Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.
arXiv.org Artificial Intelligence
May-2-2022
- Genre:
- Research Report > New Finding (1.00)
- Overview (1.00)
- Technology:
- Information Technology > Artificial Intelligence
- Representation & Reasoning
- Uncertainty > Bayesian Inference (1.00)
- Optimization (1.00)
- Machine Learning
- Neural Networks > Deep Learning (1.00)
- Statistical Learning > Regression (0.67)
- Performance Analysis > Accuracy (0.67)
- Learning Graphical Models > Directed Networks
- Bayesian Learning (1.00)
- Representation & Reasoning
- Information Technology > Artificial Intelligence