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.
Long short-term memory.Neural Computation, 9(8):1735–1780, 1997
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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.