Artificial neural network in cosmic landscape

DOI10.1007/JHEP12(2017)149zbMath1383.85016arXiv1707.02800MaRDI QIDQ1707813

Junyu Liu

Publication date: 3 April 2018

Published in: Journal of High Energy Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1707.02800

zbMATH Keywords

models of quantum gravitycosmology of theories beyond the SMrandom systems

Mathematics Subject Classification ID

Relativistic cosmology (83F05) Applications of statistics to physics (62P35) Quantization of the gravitational field (83C45) Relativistic gravitational theories other than Einstein's, including asymmetric field theories (83D05) Astrophysical cosmology (85A40)

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Cites Work

• On Gaussian random supergravity
• Quantum computation vs. firewalls
• Computational complexity of the landscape. I.
• On the geometry of the string landscape and the swampland
• Computational complexity of the landscape. II: Cosmological considerations
• Evolving neural networks with genetic algorithms to study the string landscape
• Machine learning in the string landscape
• Der Maßbegriff in der Theorie der kontinuierlichen Gruppen
• de Sitter vacua in string theory
• THE COSMOLOGICAL CONSTANT PROBLEM AND INFLATION IN THE STRING LANDSCAPE
• Inflationary universe: A possible solution to the horizon and flatness problems
• A logical calculus of the ideas immanent in nervous activity
• Approximation by superpositions of a sigmoidal function
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