论文标题
一种无监督的深度学习方法，用于求解部分整数差异方程
An unsupervised deep learning approach in solving partial integro-differential equations
论文作者
论文摘要
我们在本文中使用无监督的深度学习研究了求解部分整数差异方程（PIDE）。对于价格选择，假设基本流程遵循征税流程，我们需要解决问题。在有监督的深度学习中，预计的标签用于训练神经网络以适合奇特的解决方案。在无监督的深度学习中，神经网络被用作解决方案，并且基于神经网络计算派德中的衍生物和积分。通过与Pide及其边界条件相匹配，神经网络提供了精确的PIDE解决方案。一旦训练，这将是计算选项值以及选项希腊人的快速。
We investigate solving partial integro-differential equations (PIDEs) using unsupervised deep learning in this paper. To price options, assuming underlying processes follow Levy processes, we require to solve PIDEs. In supervised deep learning, pre-calculated labels are used to train neural networks to fit the solution of the PIDE. In an unsupervised deep learning, neural networks are employed as the solution, and the derivatives and the integrals in the PIDE are calculated based on the neural network. By matching the PIDE and its boundary conditions, the neural network gives an accurate solution of the PIDE. Once trained, it would be fast for calculating options values as well as option Greeks.
