Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials
Abstract We propose a deep neural network (DNN) as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials. We show that the DNN predicts the local stresses with 3.8% mean absolute percentage error (MAPE) for the case of heterogeneous elastic media and a mechanical...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-021-00571-z |