Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder-Decoder Learning

arXiv:2201.00722v1 Announce Type: cross
Abstract: This work presents a machine learning approach to predict peak-stress clusters in heterogeneous polycrystalline materials. Prior work on using machine learning in the context of mechanics has largely focused on predicting the effective response and overall structure of stress fields. However, their ability to predict peak stresses -- which are of critical importance to failure -- is unexplored, because the peak-stress clusters occupy a small spatial volume relative to the entire domain, and hence requires …

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