Orateur
Magdalena Larfors
Description
Calabi Yau (CY) manifolds are used ubiquitously in research on string theory. Since decades, these spaces have provided the main avenue to connect string theory with observable physics. A stumbling block in these constructions is the lack of an analytical expression for the CY metrics. In this talk I will review recent work on obtaining numerical approximations of CY metrics using machine learning, and the prospects such metrics may have in furthering string theory research.
