论文标题
来自Holomorphic Networks的数值Calabi-yau指标
Numerical Calabi-Yau metrics from holomorphic networks
论文作者
论文摘要
我们提出了用于计算数字calabi-yau（RicciflatKähler）指标的机器学习启发的方法，并使用TensorFlow/keras实现它们。我们将它们与以前的工作进行了比较，并发现它们对于很少或没有对称性的歧管更准确。我们还讨论了过度参数化和优化方法的选择等问题。
We propose machine learning inspired methods for computing numerical Calabi-Yau (Ricci flat Kähler) 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.
