Evolving neural networks with genetic algorithms to study the string landscape

A bstract 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 suc...

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Vydané v:The journal of high energy physics Ročník 2017; číslo 8; s. 1 - 20
Hlavný autor: Ruehle, Fabian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017
Springer Nature B.V
SpringerOpen
Predmet:
Artificial neural networks
Classical and Quantum Gravitation
Computation
Elementary Particles
Evolution
Genetic algorithms
High energy physics
Neural networks
Phenomenology
Physics
Physics and Astronomy
Quantum Field Theories
Quantum Field Theory
Quantum Physics
Regular Article - Theoretical Physics
Relativity Theory
String Theory
Strings
Superstring Vacua
Superstrings and Heterotic Strings
Superstring Vacua
Superstrings and Heterotic Strings
ISSN:1029-8479, 1029-8479
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Shrnutí:A bstract 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) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1029-8479
1029-8479
DOI:10.1007/JHEP08(2017)038