A regularized phenomenological multiscale damage model

By Yang Liu, Vasilina Filonova, N. Hu, Zifeng Yuan, Jacob Fish, Zheng Yuan, Ted Belytschko. 2014

48 citations recorded by the source corpus.

Subfields: Mechanics of Materials

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In Mechanics of Materials

1. Chengping Rao
2. Yang Liu

Citations in the corpus

• Physics informed deep learning for computational elastodynamics without labeled data (2020, background): “ve the data-driven solution when measurements are available [9], (2) the convenience to formulate the inverse problem in mechanics (e.g., crack detection [62{64] and material damage model calibration [65]) due to the optimization nature of training PINN [9], and (3) the capability to conduct uncerta”
• Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization (2020, methodology): “These methods have been shown to be effective to model multiscale material behavior in both linear [14, 15, 20] and nonlinear [9–12, 20–23] problems given the properly defined material constituents and microstructure.”