报告题目（Title）：Machine Learning and Physics

报告人（Speaker）：尤亦庄 Assistant Professor (UC San Diego)

报告时间（Time）：2021年1月8日（周五）14:00

报告地点（Place）：腾讯会议（会议ID：851 631 900）

                                https://meeting.tencent.com/s/uytFSiAYCpjL

邀请人（Inviter）：吴绍锋 教授

摘要（Abstract）：

In this talk, I will introduce our recent works on applying machine learning to uncover the holographic geometry from quantum entanglement, and to construct the optimal renormalization group transformation for quantum field theories. We employ unsupervised machine learning approaches that allows machine to generate observation data on the holographic boundary with variational models in the holographic bulk. By training the neural network to reproduce the observation data, the machine will be able to establish geometry/physics description for the bulk. More generally, this approach will be able to build theoretical models from observations.

报告人简介：

尤亦庄，加利福尼亚大学圣迭戈分校助理教授。研究领域为理论凝聚态物理，强关联系统，量子拓扑物态，多体局域化与热化，纠缠，张量网络，以及全息原理与机器学习。

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