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Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm

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Motivated by recent progress in the problem of numerical Kähler metrics, we survey machine learning techniques in this area, discussing both advantages and drawbacks. We then present a novel approach to obtaining Ricci-flat approximations to Kähler metrics, applying machine learning within a `principled’ framework, inspired by the algebraic ansatz of Donaldson.

This talk is part of the Accelerate Lunchtime Seminar Series series.

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