9 papers
HERMES: a multi-agent framework for structured knowledge extraction from ultra-long documents in geoscience
Ziqi Song, Zongyuan Xiang, James G. Ogg +12
Authoritative scientific knowledge in geoscience remains largely trapped in legacy monographs and historical literature, where unstructured text and complex layouts hinder computat…
Global existence analysis for a class of compressible Navier-Stokes-Korteweg equations
Ansgar Jüngel, Flora Philipp
The existence of global weak solutions to a broad class of Navier-Stokes-Korteweg equations is established for large data in the three-dimensional torus, including the diffuse-inte…
Melting of heavy quarkonia in QGP using deep neural networks
Mohammad Yousuf Jamal, Fu-Peng Li, Long-Gang Pang +1
Machine learning techniques have emerged as powerful tools for tackling non-perturbative challenges in quantum chromodynamics. In this study, we introduce a data-driven framework e…
Physics-Informed Neural Network with Squeeze-Excitation-like Attention
Yun-Fei Song, Long-Gang Pang, Fu-Peng Li +1
We introduce SEA-PINN, a novel architecture that incorporates a Squeeze-Excitation-like attention mechanism into physics-informed neural networks to dynamically recalibrate the imp…
Four-dimensional QCD equation of state from a quasi-parton model with physics-informed neural networks
Fu-Peng Li, Long-Gang Pang, Guang-You Qin
The equation of state (EoS) of strongly interacting matter at finite temperature and chemical potentials (baryon, charge, and strangeness) is a crucial input for hydrodynamic simul…
Physics-Informed Global Extraction of the Universal Small- Dipole Amplitude
Si-Wei Dai, Fu-Peng Li, Long-Gang Pang +4
We extract the universal small- dipole scattering amplitude from a global analysis based on a physics-informed neural network (PINN), without imposing a priori MV-typ…