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Model reduction for the material point method via an implicit neural representation of the deformation map
[article]
2023
arXiv
pre-print
This work proposes a model-reduction approach for the material point method on nonlinear manifolds. Our technique approximates the kinematics by approximating the deformation map using an implicit neural representation that restricts deformation trajectories to reside on a low-dimensional manifold. By explicitly approximating the deformation map, its spatiotemporal gradients – in particular the deformation gradient and the velocity – can be computed via analytical differentiation. In contrast
arXiv:2109.12390v5
fatcat:ug7vresvdzbajlflsrvmct5544