Speaker
Mohammad Sarkari Khorrami
(Max-Planck Institut für Eisenforschung)
Description
We developed a machine-learning-based approach for solving computing the elastoplastic mechanical response of polycrystalline structures. In particular, a recursive deep neural network based on U-Net and applied recursively is proposed as a surrogate model for predicting the von Mises stress field under quasi-static tensile loading. We show that the model can accurately predict both the average response as well as the local von Mises stress field in the history-dependent elastoplastic problems. The trained model can predict the nonlinear mechanical response of any grain structure, orders of magnitude faster than conventional numerical approaches such as the spectral solvers.
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Primary authors
Mohammad Sarkari Khorrami
(Max-Planck Institut für Eisenforschung)
Dr
Jaber Mianroodi
Dr
Nima Siboni
Dierk Raabe
(Max-Planck Institut für Eisenforschung)
Peter Benner
Pawan Goyal
(Max Planck Institute for Dynamics of Complex Technical Systems)
