A discretization-plus-coarse-graining scheme turns continuous-space interacting particles into a tensor-network-representable lattice model, enabling partition-function calculations for the 2D hard-disk problem.
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In the large-n limit of the O(n) model on graphs, free energy at low T is governed by the Laplacian spectrum and at high T by the adjacency spectrum.
Transition path sampling with a three-state kinetic model quantifies how topological disorder affects transition rates in Ising models on random graphs, using instance-dependent temperature rescaling to unify finite-size scaling in Erdős-Rényi graphs.
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Statistical mechanics in continuous space with tensor network methods
A discretization-plus-coarse-graining scheme turns continuous-space interacting particles into a tensor-network-representable lattice model, enabling partition-function calculations for the 2D hard-disk problem.
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From Laplacian-to-Adjacency Matrix for Continuous Spins on Graphs
In the large-n limit of the O(n) model on graphs, free energy at low T is governed by the Laplacian spectrum and at high T by the adjacency spectrum.
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Transition path sampling in Ising models on heterogeneous graphs
Transition path sampling with a three-state kinetic model quantifies how topological disorder affects transition rates in Ising models on random graphs, using instance-dependent temperature rescaling to unify finite-size scaling in Erdős-Rényi graphs.