AInstein: numerical Einstein metrics via machine learning

A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry assumptions. The model architecture contains subnetworks for each patch in the manifold-defining atlas. Each subnetwork pr...

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Autors principals:	Edward Hirst, Tancredi Schettini Gherardini, Alexander G Stapleton
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2025-01-01
Col·lecció:	AI for Science
Matèries:	Einstein metrics machine learning AI geometry AInstein Einstein equations
Accés en línia:	https://doi.org/10.1088/3050-287X/ae1117
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