185 citations · 1.3k across the 49 of their papers we have counts for
8 papers · 1 filter
Learning Langevin dynamics with QCD phase transition
Lingxiao Wang, Lijia Jiang, Kai Zhou
In this proceeding, the deep Convolutional Neural Networks (CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin proce…
Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
Yongjia Wang, Fupeng Li, Qingfeng Li +2
A deep convolutional neural network (CNN) is developed to study symmetry energy effects by learning the mapping between the symmetry energy and the two-dimensional…
An equation-of-state-meter for CBM using PointNet
Manjunath Omana Kuttan, Kai Zhou, Jan Steinheimer +2
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify th…
Shared Data and Algorithms for Deep Learning in Fundamental Physics
Lisa Benato, Erik Buhmann, Martin Erdmann +10
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle phys…
Detecting Chiral Magnetic Effect via Deep Learning
Yuan-Sheng Zhao, Lingxiao Wang, Kai Zhou +1
The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to…
Machine learning based approach to fluid dynamics
Kirill Taradiy, Kai Zhou, Jan Steinheimer +3
We study the applicability of a Deep Neural Network (DNN) approach to simulate one-dimensional non-relativistic fluid dynamics. Numerical fluid dynamical calculations are used to g…
Heavy quark potential in quark-gluon Plasma: Deep neural network meets lattice quantum chromodynamics
Shuzhe Shi, Kai Zhou, Jiaxing Zhao +2
Bottomonium states are key probes for experimental studies of the quark-gluon plasma (QGP) created in high-energy nuclear collisions. Theoretical models of bottomonium productions…
Deep learning stochastic processes with QCD phase transition
Lijia Jiang, Lingxiao Wang, Kai Zhou
It is non-trivial to recognize phase transitions and track dynamics inside a stochastic process because of its intrinsic stochasticity. In this paper, we employ the deep learning m…