Parametric Matrix Models

no code implementations • 22 Jan 2024 • Patrick Cook, Danny Jammooa, Morten Hjorth-Jensen, Daniel D. Lee, Dean Lee

We present a general class of machine learning algorithms called parametric matrix models.

Machine Learning in Nuclear Physics

no code implementations • 4 Dec 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

Advances in machine learning methods provide tools that have broad applicability in scientific research.

BIG-bench Machine Learning

Self-learning Emulators and Eigenvector Continuation

no code implementations • 28 Jul 2021 • Avik Sarkar, Dean Lee

The key ingredient is a fast estimate of the emulator error that becomes progressively more accurate as the emulator is improved, and the accuracy of the error estimate can be corrected using machine learning.

Active Learning Self-Learning