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dc.contributor.author
Fernandez, Ariel  
dc.date.available
2024-12-04T18:10:48Z  
dc.date.issued
2023  
dc.identifier.citation
Fernandez, Ariel; Querying Artificial Intelligence on the Dark Universe in a Quintessential Encoding of Space-time; Cambridge Scholars Publishing; 2023; 192  
dc.identifier.isbn
978-1-5275-3117-8  
dc.identifier.uri
http://hdl.handle.net/11336/249514  
dc.description.abstract
This book explores the possibility of the use of artificial intelligence (AI) to solve one of the cosmos’ biggest mysteries: the nature of undetectable forms of matter, namely dark matter and dark energy, which make up 95% of the universe. The book describes the outcome of this quest in terms of an entangled ur-universe that admits no observer, and incorporates an extra dimension to encode space-time as a latent manifold. A cosmic engine fueled by dark energy that maintains the topology of the universe during its expansion, involving autocatalytic vacuum creation, is identified. The physical picture of the cosmos presented in the book paves the way for a solution to the cosmological constant problem and provides a cogent explanation for the huge gap between the predicted and measured values that has troubled physicists for decades.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Cambridge Scholars Publishing  
dc.rights
info:eu-repo/semantics/closedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Artificial Intelligence  
dc.subject
Space-time  
dc.subject
Dark Sector in the Standard Model  
dc.subject
Cosmology  
dc.subject.classification
Otras Ciencias de la Computación e Información  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Querying Artificial Intelligence on the Dark Universe in a Quintessential Encoding of Space-time  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.type
info:eu-repo/semantics/book  
dc.type
info:ar-repo/semantics/libro  
dc.date.updated
2024-11-25T16:10:04Z  
dc.journal.pagination
192  
dc.journal.pais
Reino Unido  
dc.journal.ciudad
Cambridge  
dc.description.fil
Fil: Fernandez, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.cambridgescholars.com/product/978-1-5275-3117-8