A Greedy Emulator for Nuclear Two-Body Scattering
Abstract
We develop an active learning emulator (i.e., surrogate model) for nuclear two-body scattering that improves its uncertainty using a greedy approach. The goal is to facilitate fast & accurate Bayesian uncertainty quantification of nuclear interactions.
Keywords:
nuclear physics, computational physics, machine learning, model order reduction, nuclear scattering
Status
Graduate
Department
Physics & Astronomy
College
College of Arts and Sciences
Campus
Athens
Faculty Mentor
Drischler, Christian
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
A Greedy Emulator for Nuclear Two-Body Scattering
We develop an active learning emulator (i.e., surrogate model) for nuclear two-body scattering that improves its uncertainty using a greedy approach. The goal is to facilitate fast & accurate Bayesian uncertainty quantification of nuclear interactions.