A physics-informed graph variational autoencoder jointly predicts modal frequencies, damping, and shapes from PSD data of trusses with uncertainty quantification and orthogonality constraints.
Deep evidential regression.Advances in neural information processing systems, 33:14927–14937, 2020
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A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
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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.
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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.