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Found 5 papers, 1 papers with code
On the uncertainty principle of neural networks
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.
Machine Learning in Nuclear Physics
Advances in machine learning methods provide tools that have broad applicability in scientific research.
A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs
We have demonstrated that Tensorflow and Numba help inexperienced scientific researchers to parallelize their programs on multiple GPUs with little work.
Computational Physics
An equation-of-state-meter of quantum chromodynamics transition from deep learning
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.
An equation-of-state-meter of QCD transition from deep learning
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 $\rho(p_T,\Phi)$.
