389 citations · 975 across the 14 of their papers we have counts for
14 papers
Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting
Shuai Han, Lukas Stelz, Thomas R. Sokolowski +2
Accurate epidemic forecasting is crucial for effective disease control and prevention. Traditional compartmental models often struggle to estimate temporally and spatially varying…
Identifying the nature of the QCD transition in heavy-ion collisions with deep learning
Yi-Lun Du, Kai Zhou, Jan Steinheimer +5
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collision…
Laser Wake Field Collider
István Papp, Larissa Bravina, Mária Csete +11
Recently NAano-Plasmonic, Laser Inertial Fusion Experiments (NAPLIFE) were proposed, as an improved way to achieve laser driven fusion. The improvement is the combination of two ba…
Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
Yi-Lun Du, Kai Zhou, Jan Steinheimer +5
Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion colli…
Radiation dominated implosion with flat target
L. P. Csernai, M. Csete, I. N. Mishustin +5
Inertial Confinement Fusion is a promising option to provide massive, clean, and affordable energy for humanity in the future. The present status of research and development is hin…
Regressive and generative neural networks for scalar field theory
Kai Zhou, Gergely Endrődi, Long-Gang Pang +1
We explore the perspectives of machine learning techniques in the context of quantum field theories. In particular, we discuss two-dimensional complex scalar field theory at nonzer…