An ALE-consistent GNO-ViT and LSTM framework with boundary correction and two-stage training achieves accurate phase-consistent long-term FSI predictions on a flexible beam benchmark with good generalization to inlet variations.
Machine learning for modelling unstructured grid data in computational physics: a review.Information Fusion, page 103255, 2025
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An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
An ALE-consistent GNO-ViT and LSTM framework with boundary correction and two-stage training achieves accurate phase-consistent long-term FSI predictions on a flexible beam benchmark with good generalization to inlet variations.