报告主题：AI for Digital Twin（人工智能融合数字孪生）
摘要：In this talk, we mainly introduce our recent studies on digital twin networks. We first present the concept and model related to Digital Twin (DT). Then, we focus on new research challenges and results when machine learning is exploited in DT, including federated learing, deep reinforcement learning and transfer learning and the applications in communications.
在本次报告中，我们主要介绍关于数字孪生网络的最新研究。首先，我们将阐述与数字孪生（DT）相关的概念和模型。随后，我们将聚焦于在数字孪生中应用机器学习时面临的新研究挑战，涵盖联邦学习、深度强化学习和迁移学习，以及在通信领域的应用。
个人简介：
张彦，欧洲科学院院士，挪威皇家科学院院士，挪威工程院院士，IEEE/IET Fellow，国家海外高层次人才入选者，科睿唯安（Clarivate Analytics）“全球高被引科学家”。现任电子科技大学教授，曾任挪威奥斯陆大学终身教授，挪威Simula国家实验室首席科学家和所长。近期主要研究方向为智能网络与通信及智能安全物联网。在相关领域研究成果被引用55000+次，H指数为120。现任IEEE TII（IEEE Transactions on Industrial Informatics）共同主编，IEEE TGCN（IEEE Transactions on Green Communications and Networking）领域编辑，IEEE Systems高级编辑，及多个IEEE Transactions/Magazine期刊编委和国内多个核心期刊编委。
Yan Zhang, Professor at University of Electronic Science and Technology of China (UESTC), IEEE Fellow, IET Fellow, and Clarivate Analytics "Highly Cited Researcher." Elected member of MAE, DKNVS, and NTVA. His recent research focuses on next-generation wireless networks and intelligent and secure Internet of Things (IoT). His work has been cited over 53,000 times with an H-index of 118. He is now serving as
Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics (IEEE TII), Area Editor for IEEE Transactions on Green Communications and Networking (IEEE TGCN), Senior Editor for IEEE Systems, and editor of multiple IEEE Transactions/Magazines and Chinese scientific journals.