arXiv Artificial Intelligence

Learning Stiffness Dependent Fluid Structure Dynamics from Coarse Flow Representations

4 hours ago 24.09.2026 07:00 👁 2 views
Learning Stiffness Dependent Fluid Structure Dynamics from Coarse Flow Representations

Quick summary

arXiv:2609.26816v1 Announce Type: cross Abstract: This paper develops a data-driven framework for long-term prediction of fluid--structure interaction (FSI) dynamics, focusing on the flow-induced vibration (FIV) of a flexible plate. A stiffness-conditioned neural evolution operator jointly represents the Eulerian flow field and Lagrangian structural state. The plate is represented by 101 ordered structural tokens carrying nodal coordinates and velocities, with nondimensional bending stiffness as a global conditioning variable. Bidirectional cross-attention couples fluid and structural represen

Key takeaways

  • arXiv:2609.26816v1 Announce Type: cross Abstract: This paper develops a data-driven framework for long-term prediction of fluid--structure interaction (FSI) dynamics, focusing on the flow-induced vibration (FIV) of a flexible plate.
  • A stiffness-conditioned neural evolution operator jointly represents the Eulerian flow field and Lagrangian structural state.
  • The plate is represented by 101 ordered structural tokens carrying nodal coordinates and velocities, with nondimensional bending stiffness as a global conditioning variable.

Why it matters

The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗