43 citations · 81 across the 6 of their papers we have counts for
13 papers
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…
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…
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…
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
Lingxiao Wang, Tian Xu, Till Hannes Stoecker +3
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evalu…
A fast centrality-meter for heavy-ion collisions at the CBM experiment
Manjunath Omana Kuttan, Jan Steinheimer, Kai Zhou +2
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CB…