A physics-informed graph variational autoencoder jointly predicts modal frequencies, damping, and shapes from PSD data of trusses with uncertainty quantification and orthogonality constraints.
Using graph neural networks and frequency domain data for automated operational modal analysis of populations of structures.Data-Centric Engineering, 6:e45
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A Physics-Aware Variational Graph Autoencoder for Joint Modal Identification with Uncertainty Quantification
A physics-informed graph variational autoencoder jointly predicts modal frequencies, damping, and shapes from PSD data of trusses with uncertainty quantification and orthogonality constraints.