AI RESEARCH
An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
arXiv CS.LG
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ArXi:2605.00937v1 Announce Type: cross We propose an arbitrary Lagrangian-Eulerian (ALE)-consistent machine learning framework for long-term fluid-structure interaction (FSI) prediction on deforming unstructured meshes. Specifically, the fluid dynamics are modeled by a surrogate that combines a graph neural operator (GNO) with a vision Transformer (ViT) for spatiotemporal prediction, while a lightweight long short-term memory (LSTM) network predicts structural kinematics at the interface. The two surrogates are coupled through a standard partitioned procedure.