A graph-attention Hamiltonian network learns particle interaction structure directly from trajectory data and uses it to predict dynamics and detect lattice defects.
KP-II approximation for a scalar FPU system on a 2D square lattice
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abstract
We consider a scalar Fermi-Pasta-Ulam (FPU) system on a square 2D lattice. The Kadomtsev-Petviashvili (KP-II) equation can be derived by means of multiple scale expansions to describe unidirectional long waves of small amplitude with slowly varying transverse modulations. We show that the KP-II approximation makes correct predictions about the dynamics of the original scalar FPU system. An existing approximation result is extended to an arbitrary direction of wave propagation. The main novelty of this work is the use of Fourier transform in the analysis of the FPU system in strain variables.
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Graph Attention Hamiltonian Neural Networks: A Lattice System Analysis Model Based on Structural Learning
A graph-attention Hamiltonian network learns particle interaction structure directly from trajectory data and uses it to predict dynamics and detect lattice defects.