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Abstract：
Quantum chromodynamics(QCD) is the fundamental theory describing visible matter in the universe, and understanding the properties of QCD matter relies on relativistic heavy-ion collision experiments, compact star observations, and lattice QCD calculations. How to extract important physics properties from experimental observations or calculations, such as phase transition behaviors, nuclear matter equation of state(EoS), transport and topological properties, etc., can be reduced to solving the corresponding inverse problems. This talk will present new developments in the use of deep learning to solve inverse problems in exploring QCD matter. In addition, I will introduce the application of deep generative models to lattice quantum field theory and discuss the intrinsic connection between the state-of-the-art diffusion models and stochastic quantization schemes.
Biography:
Lingxiao Wang got his Ph.D. from Tsinghua University in 2020, during which he spent one year at the University of Tokyo as a joint Ph.D. From 2020 to 2023, he worked as a postdoctoral researcher at the Frankfurt Institute for Advanced Studies(FIAS) and concurrently served as an assistant researcher at Frankfurt University. He then became a research scientist at RIKEN in 2024. His main research fields focus on the application of machine learning in exploring QCD physics, including the lattice quantum field theory (LQFT), the properties of dense nuclear matter, and QCD phase transitions. Additionally, he is also devoted to AI for Science from a multidisciplinary perspective.
Host: Prof. Yifeng Sun
Alternative online link: https://meeting.tencent.com/dm/SChvRPBbPdka
(id: 306532024 passcode: 123456）