Speaker
Description
In this talk I will demonstrate how we can use machine learning based computational paradigm to help our exploration of QCD matter in extreme conditions. The focus is about properties of hot and dense nuclear matter related studies. Around it, experimentally the relativistic nuclear collision are performed to realize the extreme conditions for studying it 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 studies. Specifically, in this talk I will introduce the potential of machine learning for research about hot and dense nuclear matter, ranging from identifying essential physics from nuclear collision experiment, to assisting the lattice QCD data analysis, and to efficiently exploiting astronomical observations in inferring the Neutron Star equation of state.
