报告题目：Exploration of Matter in extreme conditions with Machine Learning
报告人: 周凯 Frankfurt Institute for Advanced Studies(FIAS)
报告地点：理科楼C302报告厅/腾讯会议ID：167 537 529
Matter under extreme conditions, e.g. high temperature and/or high density, can reveal its basic constituents. We will focus on the properties study of hot and dense nuclear matter here, which forms the current deepest material form, quark-gluon plasma (QGP). Around it, experimentally the relativistic nuclear collision are performed to realize the extreme conditions for studying this hot and dense nuclear matter, while theoretically the first-principle lattice field theory constructs the main path to investigate the equilibrium thermodynamics of the matter. Meanwhile, the astronomical observations on Neutron Star also provide constraints on the equation of state of the dense nuclear matter. Machine Learning within the broadly Artificial Intelligence (AI) brand is a rapidly developing field that has been proven to be powerful in recognizing patterns from complex data, and powerful as well in representing relationships/mappings of systems. This modern computation technologies has become increasingly prominent in all sectors of our everyday life, and also into frontiers of scientific research especially in computational related aspects. In this talk I will introduce the potential of machine learning within hot and dense nuclear matter's study, ranging from identifying essential physics from nuclear collision experiment, to assisting the lattice field theory calculation, and to more efficiently exploiting astronomical observations data in inferring the Neutron Star equation of state.
Dr. Kai Zhou received his B.Sc. degree in Physics from Xi'an Jiaotong University in 2009, and his PhD degree in physics from Tsinghua University (Superviser: Prof. Pengfei Zhuang) with "Wu You Xun" Honors in 2014. Afterwards he went to Goethe University for his Postdoctoral research in the Institute for Theoretical Physics (ITP). Since 2017, he joined FIAS as Research Fellow and lead the group "Deepthinkers" focusing on Deep Learning (DL) for physics and beyond, and since 2021 he became fellow at FIAS. Dr. Zhou has a very broad interest in physics and AI/DL application in different fields, particularly developing data-driven and physics-informed deep learning methods for physics research.
