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Title:
Numerical metrics for Calabi-Yau and other reduced holonomy manifolds
Speaker:
Abstract:
I will explain how to leverage neural networks to approximate Calabi-Yau or SU(3) structure metrics. We vastly extend previous work in this area to provide approximations for manifolds that can be described as hypersurfaces in toric varieties or complete intersections in projective spaces. While extensions to the latter are rather straight-forward, the former require more work. I will first explain how to obtain a metric that servers as a starting point, and then how to sample points uniformly from these spaces with respect to this metric.
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