Fig. 8: Overview of the PI-GNN architecture and training loss function.
From: Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning
a Physics-informed graph neural network structure for learning the mobility law in Discrete Dislocation Dynamics (DDD), which consists of three different networks: F-net, B-net, and E-net. Each network consists of hidden layers, which are stacked HeteroSage GNNs, and physical layers, which are physically inspired functions adopted from a phenomenological description of dislocation mobility. b The proposed HeteroSage GNN structure and message passing diagram for heterogeneous graphs. c, d Illustrations of the proposed generalized loss function for measuring the distance between two dislocation configurations with different total number of nodes resulting from different discretization.
