Title: Machine learning the dimension of a Fano variety
Speaker: Sara Veneziale
Abstract: The use of machine learning and data analysis techniques in pure mathematics for conjecture formulation has been a growing area of research, with examples in knot theory, representation theory, and string theory. In this talk, we go through a successful example of such an application in algebraic geometry, which positions itself in the context of mirror symmetry for Fano varieties. We study the quantum period, a conjectured invariant of Fanos, for toric Fano varieties and build models that can predict the dimension of a variety from it. This inspires and justifies the construction of rigorous asymptotics that prove, in this context, the relationship between quantum period and dimension.
Some snacks will be provided before and after the talk.