首页
Discriminative and Generative AI for High Energy Nuclear Physics
报告简介：
This talk will focus on AI meets High Energy Nuclear Physics from inverse problem solving and deep generative modelling, to introduce the methodologies of machine learning used in exploring QCD matter under extreme conditions. Related to the inverse problem solving for QCD matter studies, supervised learning, Bayesian Inference and automatic differentiation based discriminative AI methods will be discussed. For Deep Generative modelling, the flow-based models and diffusion models will be discussed for their usage in lattice field theory and heavy ion collisions simulations.
报告人简介：
周凯
Dr. Kai Zhou received his B.Sc. from Xi'an Jiaotong University (2009) and Ph.D. in Physics from Tsinghua University (2014). He subsequently worked as a Postdoctoral Researcher at Goethe University Frankfurt (2014–2017) and later became a Research Fellow and Group Leader (W1/W2) at the Frankfurt Institute for Advanced Studies, where he led the “Deepthinkers” AI-for-Science group. In 2023, he joined CUHK-Shenzhen as an Assistant Professor. His research focuses on applying modern machine learning and deep learning methods to fundamental problems in physics.
