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(29.04.) Numerical Calabi-Yau metrics from holomorphic networks

Mike Douglas (CMSA Harvard and Stony Brook )

29.04.2021 at 16:15 

We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat Kahler) metrics, and implement them using Tensorflow/Keras. We compare them with previous work, and find that they are far more accurate for manifolds with little or no symmetry. We also discuss issues such as overparameterization and choice of optimization methods. Joint work with Yidi Qi and Subramanian Lakshminarasimhan.

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