Theoretische Physik – Gruppe Zhou


Sunda-arc seismicity: continuing increase of high-magnitude earthquakes since 2004

N Srivastava, OE Sayed, M Chakraborty, J Köhler, J Steinheimer, J Faber, ...

arXiv preprint arXiv:2108.06557


Learning Langevin dynamics with QCD phase transition

Lingxiao Wang, Lijia Jiang, Kai Zhou

Contribution to: SQM2021 • e-Print: 2108.03987 [nucl-th]


Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning

Y Wang, F Li, Q Li, H Lü, K Zhou

arXiv preprint arXiv:2107.11012


Deep learning stochastic processes with QCD phase transition

Lijia Jiang, Lingxiao Wang, Kai Zhou

Published in: Phys.Rev.D 103 (2021) 11, 116023 • e-Print: 2103.04090 [nucl-th]


An equation-of-state-meter for CBM using PointNet

MO Kuttan, K Zhou, J Steinheimer, A Redelbach, H Stoecker

arXiv preprint arXiv:2107.05590


Shared Data and Algorithms for Deep Learning in Fundamental Physics

L Benato, E , Buhmann, M Erdmann, P Fackeldey, J Glombitza, ...

arXiv preprint arXiv:2107.00656


Machine learning based approach to fluid dynamics

K Taradiy, K Zhou, J Steinheimer, R V.Poberezhnyuk, V Vovchenko, ...

arXiv preprint arXiv:2106.02841


Detecting Chiral Magnetic Effect via Deep Learning

Y Zhao, L Wang, K Zhou, X Huang

arXiv preprint arXiv:2105.13761


Heavy Quark Potential in QGP: DNN meets LQCD

S Shi, K Zhou, J Zhao, S Mukherjee, P Zhuang

arXiv preprint arXiv:2105.07862


Unsupervised outlier detection in heavy-ion collisions

P Thaprasop, K Zhou, J Steinheimer, C Herold

Physica Scripta 96 (6), 064003


Deep learning stochastic processes with QCD phase transition

LJ Jiang, LX Wang, K Zhou

arXiv preprint arXiv:2103.04090


Deep Learning Based Impact Parameter Determination for the CBM Experiment

MO Kuttan, J Steinheimer, K Zhou, A Redelbach, H Stoecker

Particles 4 (1), 47-52


A fast centrality-meter for heavy-ion collisions at the CBM experiment

MO Kuttan, J Steinheimer, K Zhou, A Redelbach, H Stoecker

Physics Letters B 811, 135872


Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk

LX Wang, T Xu, TH Stoecker, H Stoecker, Y Jiang, K Zhou

Mach. Learn.: Sci. Technol. 2 (2021) 035031,  arXiv preprint arXiv:2012.00082


Neural Network Statistical Mechanics

L Wang, Y Jiang, K Zhou

arXiv preprint arXiv:2007.01037


Heavy flavors under extreme conditions in high energy nuclear collisions

J Zhao, K Zhou, S Chen, P Zhuang

Prog.Part.Nucl. Phys. 114 (2020) 103801


Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

Yi-Lun Du, Kai Zhou, Jan Steinheimer, Long-Gang Pang, Anton Motornenko et al. 

Eur.Phys.J.C 80 (2020) 6, 516 

A machine learning study to identify spinodal clumping in high energy nuclear collisions

Jan Steinheimer, Longgang Pang, Kai Zhou, Volker Koch, Jørgen Randrup et al. 

JHEP 12 (2019) 122 


Recognizing the topological phase transition by Variational Autoregressive Networks

LX Wang, Y Jiang, LY He, K Zhou

arXiv preprint arXiv:2005.04857


Regressive and generative neural networks for scalar field theory

Kai Zhou, Gergely Endrődi, Long-Gang Pang, Horst Stöcker

Phys.Rev.D 100 (2019) 1, 011501 


An equation-of-state-meter of quantum chromodynamics transition from deep learning

Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-nian Wang

Nature Commun. 9 (2018) 1, 210