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Distilling ML Models into Formulae for Ricci-Flat Metrics

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If you have a question about this talk, please contact Sam Nallaperuma-Herzberg .

Machine learning has shown great success in approximating Ricci-flat metrics on Calabi–Yau manifolds, but its black-box nature often limits interpretability. In this talk, I will show that for highly symmetric manifolds, the machine learning models used to approximate these metrics can be distilled into closed-form symbolic expressions. These expressions are compact, interpretable, and have the same accuracy as the original model.

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

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