About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Statistical Generation of Three-Dimensional Dislocation Microstructures with Graph Neural Networks |
Author(s) |
Dylan Madisetti, Jafaar El-Awady, Christopher Stiles |
On-Site Speaker (Planned) |
Dylan Madisetti |
Abstract Scope |
Building upon recent success in using Graph Neural Networks (GNNs) to model protein folding, we explore machine learning graph-based techniques for predicting three-dimensional (3D) dislocation networks in metals from a variety of material signals (e.g., X-Ray diffraction patterns, plastic response). Dislocation networks are represented as directed spatial graphs, where the directed edges represent dislocation lines (with metadata such as Burgers vector) connected at vertices (dislocation nodes). Adjacency matrices, constructed from sub-volumes of the dislocation network, are used as inputs to a GNN, which encodes connectivity into a latent vector. Material signals are then associated with this latent vector, allowing for fast microstructure similarity lookup. Additionally, the latent vector is used to produce new, statistically similar microstructures. This dual ability allows for the generation of representative dislocation networks from experiments, enabling a deeper understanding of dislocation evolution during loading with forward simulation in 3D Discrete Dislocation Dynamics |
Proceedings Inclusion? |
Planned: |