Deep learning: Extrapolation tool for ab initio nuclear theory
| Autor: | Gianina Alina Negoita, Esmond G. Ng, Chao Yang, Pieter Maris, Matthew Lockner, Ik Jae Shin, Andrey M. Shirokov, Glenn R. Luecke, G. M. Prabhu, Youngman Kim, James P. Vary |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Physics
FOS: Computer and information sciences Computer Science - Machine Learning Basis (linear algebra) Nuclear Theory Ab initio Extrapolation FOS: Physical sciences Observable Space (mathematics) Machine Learning (cs.LG) Nuclear Theory (nucl-th) Limit (mathematics) Statistical physics Independence (probability theory) Energy (signal processing) |
| Popis: | Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The NCSM and other approaches require an extrapolation of the results obtained in a finite basis space to the infinite basis space limit and assessment of the uncertainty of those extrapolations. Each observable requires a separate extrapolation and most observables have no proven extrapolation method. We propose a feed-forward artificial neural network (ANN) method as an extrapolation tool to obtain the ground state energy and the ground state point-proton root-mean-square (rms) radius along with their extrapolation uncertainties. The designed ANNs are sufficient to produce results for these two very different observables in $^6$Li from the ab initio NCSM results in small basis spaces that satisfy the following theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. Comparisons of the ANN results with other extrapolation methods are also provided. 13 pages, 6 figures. Some typos were fixed, e.g., replaced MSE units for the observables with observables' square units. arXiv admin note: text overlap with arXiv:1803.03215 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|