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Evolving neural networks with genetic algorithms to study the string landscape

Abstract:

We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) app...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1007/JHEP08(2017)038

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Physics; Theoretical Physics
ORCID:
0000-0002-8409-9823
Publisher:
Springer Publisher's website
Journal:
Journal of High Energy Physics Journal website
Volume:
2017
Issue:
8
Pages:
38
Publication date:
2017-08-09
Acceptance date:
2017-07-28
DOI:
EISSN:
1029-8479
ISSN:
1126-6708
Pubs id:
pubs:724502
URN:
uri:90b926ec-de62-4807-836d-0c48e85fb602
UUID:
uuid:90b926ec-de62-4807-836d-0c48e85fb602
Local pid:
pubs:724502

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