3 papers
cs.LG2026
Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
Ru Geng, Panayotis Kevrekidis, Yixian Gao +2
Lattice Hamiltonian systems underpin models across condensed matter, nonlinear optics, and biophysics, yet learning their dynamics from data is obstructed by two unknowns: the inte…
hep-lat2024
Graph Attention Hamiltonian Neural Networks: A Lattice System Analysis Model Based on Structural Learning
Ru Geng, Yixian Gao, Jian Zu +1
A deep understanding of the intricate interactions between particles within a system is a key approach to revealing the essential characteristics of the system, whether it is an in…
math.DS2024
-SGHN: A Robust Model for Learning Particle Interactions in Lattice Systems
Yixian Gao, Ru Geng, Panayotis Kevrekidis +2
We propose an -separable graph Hamiltonian network (-SGHN) that reveals complex interaction patterns between particles in lattice systems. Utilizing trajectory data, -S…