报告题目：Learning single-particle mobility edges by a data-compression-based neural network

报告人： 柏小东 助理研究员（鹏城实验室）

报告时间：2021 年 01 月 28 日（周四）15：30          

报告地点：在线报告（ 腾讯会议 ID : 817 509 471）

报告摘要：

The breaking of the well-known Aubry-Andre model with a single-particle mobility edge (SPME) is a intriguing subject that has not yet been fully understood. In particular, how to accurately and efficiently recognize a SPME in optical lattice is currently under active debate. In this work, we develop a data-compression-based neural-network (DCNN) approach to identify SPMEs in one dimensional quasi-periodic optical lattice using eigenstates as the sole diagnostic. We find that such method can successfully identify the SPMEs of large system only using the small network trained by small system data, without onerously and repetitively training a new and large-scale network by massive data of large system.

报告人简介：

柏小东，鹏城实验室前沿研究中心助理研究员，2017年博士毕业于北京师范大学，2017-2019年在中科院理论物理研究所从事博士后研究。研究方向包含超冷量子气体在光晶格中的输运特性，无序晶格中的多体局域化现象以及机器学习在超冷量子气体中的应用特性。目前的研究兴趣主要集中在机器学习和多体局域化现象领域。

参考文献：

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