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Complexity and Random Matrix Approximations

<!--HTML-->Efforts to better understand the landscape and swampland can be stifled by computational complexity. I will discuss ways in which complexity could be overcome by learning random matrix approximations to string data, including both opportunities and caveats. As a concrete example, ge...

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Detalles Bibliográficos
Autor principal: Halverson, James
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2682615
Descripción
Sumario:<!--HTML-->Efforts to better understand the landscape and swampland can be stifled by computational complexity. I will discuss ways in which complexity could be overcome by learning random matrix approximations to string data, including both opportunities and caveats. As a concrete example, generative adversarial networks will be used to learn random matrix approximations to certain Calabi-Yau data, and physical implications will be discussed.