Extrapolation of nuclear structure observables with artificial neural networks
Abstract
Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in 4He, 6Li, and 16O, and compare the predictions from neural networks with results from infrared extrapolations.
- Authors:
-
[1];
[1]
- Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Nuclear Physics (NP)
- OSTI Identifier:
- 1649099
- Alternate Identifier(s):
- OSTI ID: 1593553
- Grant/Contract Number:
- AC05-00OR22725; FG02-96ER40963; SC0018223
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review. C
- Additional Journal Information:
- Journal Volume: 100; Journal Issue: 5; Journal ID: ISSN 2469-9985
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Nuclear structure & decays; artificial neural networks; tensor network methods
Citation Formats
Jiang, W. G., Hagen, G., and Papenbrock, T. Extrapolation of nuclear structure observables with artificial neural networks. United States: N. p., 2019.
Web. doi:10.1103/physrevc.100.054326.
Jiang, W. G., Hagen, G., & Papenbrock, T. Extrapolation of nuclear structure observables with artificial neural networks. United States. https://doi.org/10.1103/physrevc.100.054326
Jiang, W. G., Hagen, G., and Papenbrock, T. Thu .
"Extrapolation of nuclear structure observables with artificial neural networks". United States. https://doi.org/10.1103/physrevc.100.054326. https://www.osti.gov/servlets/purl/1649099.
@article{osti_1649099,
title = {Extrapolation of nuclear structure observables with artificial neural networks},
author = {Jiang, W. G. and Hagen, G. and Papenbrock, T.},
abstractNote = {Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in 4He, 6Li, and 16O, and compare the predictions from neural networks with results from infrared extrapolations.},
doi = {10.1103/physrevc.100.054326},
journal = {Physical Review. C},
number = 5,
volume = 100,
place = {United States},
year = {Thu Nov 21 00:00:00 EST 2019},
month = {Thu Nov 21 00:00:00 EST 2019}
}
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