4 papers
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…
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…
Vertex Weight Reconstruction in the Gel'fand's Inverse Problem on Connected Weighted Graphs
Songshuo Li, Yixian Gao, Ru Geng +1
We consider the reconstruction of the vertex weight in the discrete Gel'fand's inverse boundary spectral problem for the graph Laplacian. Given the boundary vertex weight and the e…
-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…