A graph neural network trained only on compression trajectories of disordered elastic networks infers Poisson's ratio and generalizes to networks with Poisson's ratios outside the training range.
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Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks
A graph neural network trained only on compression trajectories of disordered elastic networks infers Poisson's ratio and generalizes to networks with Poisson's ratios outside the training range.