Abstract
Recent progress in Graph Neural Networks (GNNs) has allowed the creation of new methods for surrogate modelling, or emulation, of complex physical systems to a high level of fidelity. The success of such methods has yet to be explored however in the context of soft-tissue mechanics, an area of research which has itself seen substantial developments in recent years. The present work explicates on this by introducing an emulation framework based on a multi-scale, message-passing GNN, before applying it to the modelling of passive left-ventricle mechanics. Through numerical experiments, it is demonstrated that the proposed method delivers strong predictive accuracy when benchmarked against the results of the nonlinear finite-element method (FEM), and significantly outperforms an alternative emulator based on a fully connected neural network. Furthermore, large computational gains are achieved at prediction time against the FEM.
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