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arXiv AI By Chun-Jun Pu, Li-Wei Chen, Hai-Bo Huang

Learning Stiffness Dependent Fluid Structure Dynamics from Coarse Flow Representations

Read the original on arXiv AI →

The paper presents a data‑driven framework that predicts long‑term fluid–structure interaction dynamics for a flexible plate undergoing flow‑induced vibration. It uses a stiffness‑conditioned neural evolution operator that jointly models the Eulerian flow field and the Lagrangian structural state, employing a hybrid CNN‑Transformer architecture with bidirectional cross‑attention. The operator accurately captures three stiffness‑dependent response regimes, preserves key flow and structural features over 1000‑step rollouts, and can interpolate to unseen stiffness values, while a differentiable aerodynamic‑force module based on derivative‑moment transformation enables accurate lift and drag reconstruction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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