Biography
Longgang Pang, Professor, male, born in May 1984, holds a Ph.D. degree. He is a recipient of the National High-Level Talent Recruitment Program. His primary research focuses on high-energy nuclear physics and artificial intelligence. He independently developed the GPU-parallelized (3+1)-dimensional relativistic viscous hydrodynamic code, CLVisc, to simulate the spatiotemporal evolution of the quark-gluon plasma (QGP) produced in high-energy nuclear collisions. He has also contributed to the development of the relativistic molecular dynamics transport code, SMASH. Dr. Pang applies advanced data analysis methods, such as Bayesian analysis and deep learning techniques from artificial intelligence, to high-energy nuclear physics. He has made significant contributions to the study of fluctuations and correlations, vorticity and polarization, the equation of state, and initial-state nuclear structure in high-energy nuclear physics. He is actively engaged in cutting-edge interdisciplinary research at the intersection of high-energy nuclear physics and artificial intelligence and has been invited to publish review articles on the application of artificial intelligence in high-energy nuclear physics in renowned international journals such as Reviews of Modern Physics and Progress in Particle and Nuclear Physics. His current research interests include relativistic hydrodynamics and the equation of state of nuclear matter, interdisciplinary studies in artificial intelligence and computational physics, applications of automatic differentiation programming and Physics-Informed Neural Networks (PINNs) in inverse and variational problems, generative models for accelerating physical simulations, and reinforcement learning with decision intelligence.
Education
2010–2012: Joint Ph.D. training, Nuclear Theory Group, Lawrence Berkeley National Laboratory, USA
2006–2009: Combined Master’s and Ph.D. program, Department of Modern Physics, University of Science and Technology of China
2002–2006: Bachelor’s degree, Department of Modern Physics, University of Science and Technology of China
Professional Experience
End of 2019–Present: Guizi Scholar, Department of Physics, Central China Normal University
2018–2019: Assistant Scientist, Department of Physics, University of California, Berkeley, USA
2015–2017: Postdoctoral Researcher, Frankfurt Institute for Advanced Studies, Germany
2013–2014: Postdoctoral Researcher, Institute of Particle Physics, Central China Normal University
Recent Publications (Last 5 Years)
6. Exploring QCD matter in extreme conditions with Machine Learning
K.Zhou, L.X.Wang, L.G.Pang, S.Z.Shi,
Prog.Part.Nucl.Phys. 135 (2024) 104084
5. Deep-learning quasi-particle masses from QCD equation of state.
F.P. Li, H.L. Lv, L.G.Pang, G.Y. Qin,
Phys.Lett.B 844 (2023) 138088
4. Deep learning assisted jet tomography for the study of Mach cones in QGP.
Z.Yang,Y.Y.He,W.Y.Ke,L.G.Pang,X.N.Wang,Eur.Phys.J.C 83 (2023) 7, 652
3. Colloquium: Machine learning in nuclear physics.
A. Boehnlein,et.al,L.G.Pang,
Rev.Mod.Phys. 94 (2022) 3, 031003
2. Shear-Induced Spin Polarization in Heavy-Ion Collisions.
B.C. Fu, S.Y.F Liu, L.G. Pang, H.C. Song, Y. Yin
Phys.Rev.Lett. 127 (2021) 14, 142301
1. Gradient Tomography of Jet Quenching in Heavy-Ion Collisions.
Y.Y. He, L.G.Pang, X.N.Wang.
Phys.Rev.Lett. 125 (2020) 12, 122301
Representative publications
1. Pseudorapidity distribution and decorrelation of anisotropic flflow within CLVisc hydrodynamics.
L. G. Pang*, H. Petersen and X. N. Wang,
Phys.Rev. C97 (2018) no.6, 064918 (CLVisc code)
2. An equation-of-state-meter of quantum chromodynamics transition from deep learning.
L. G. Pang*, K. Zhou*, N. Su*, H. Petersen, H. Stoecker and X. N. Wang.
Nature Communications, volume 9, Article number: 210 (2018)
3. Vortical fluid andΛspin correlations in high-energy heavy-ion collisions.
L. G. Pang, H. Petersen, Q. Wang and X. N. Wang.
Phys.Rev.Lett. 117 (2016) no.19, 192301.
4. Bayesian extraction of jet energy loss distributions in heavy-ion collisions.
Y.Y. He, L.G.Pang*, X.N.Wang*.
Phys.Rev.Lett. 122 (2019) no.25, 252302
5. Microscopic study of deuteron production in PbPb collisions at 2.76 TeV via
hydrodynamics and a hadronic afterburner.
D. Oliinychenko, L. G. Pang, H. Elfner and V. Koch
Phys.Rev.C 99, 044907 (2019). (Editor recommendation and featured in Physics)
