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Title: Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory

Authors:
ORCiD logo; ORCiD logo; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1605311
Grant/Contract Number:  
[FG-02-05ER41375]
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Physical Review E
Additional Journal Information:
[Journal Name: Physical Review E Journal Volume: 101 Journal Issue: 3]; Journal ID: ISSN 2470-0045
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English

Citation Formats

Windisch, Andreas, Gallien, Thomas, and Schwarzlmüller, Christopher. Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory. United States: N. p., 2020. Web. doi:10.1103/PhysRevE.101.033305.
Windisch, Andreas, Gallien, Thomas, & Schwarzlmüller, Christopher. Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory. United States. doi:10.1103/PhysRevE.101.033305.
Windisch, Andreas, Gallien, Thomas, and Schwarzlmüller, Christopher. Wed . "Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory". United States. doi:10.1103/PhysRevE.101.033305.
@article{osti_1605311,
title = {Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory},
author = {Windisch, Andreas and Gallien, Thomas and Schwarzlmüller, Christopher},
abstractNote = {},
doi = {10.1103/PhysRevE.101.033305},
journal = {Physical Review E},
number = [3],
volume = [101],
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
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