no code implementations • 3 May 2022 • Jun-Jie Zhang, Dong-Xiao Zhang, Jian-Nan Chen, Long-Gang Pang, Deyu Meng
Despite the successes in many fields, it is found that neural networks are difficult to be both accurate and robust, i. e., high accuracy networks are often vulnerable.
no code implementations • 4 Dec 2021 • Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler
Advances in machine learning methods provide tools that have broad applicability in scientific research.
1 code implementation • 21 Feb 2019 • Hong-Zhong Wu, Jun-Jie Zhang, Long-Gang Pang, Qun Wang
We have demonstrated that Tensorflow and Numba help inexperienced scientific researchers to parallelize their programs on multiple GPUs with little work.
Computational Physics
no code implementations • 15 Jan 2018 • Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions.
no code implementations • 13 Dec 2016 • Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra $\rho(p_T,\Phi)$.