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Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction
arXiv – CS AI|Shilong Tao, Zhe Feng, Shaohan Chen, Weichen Zhang, Zhanxing Zhu, Yunhuai Liu||5 views
🤖AI Summary
Researchers have developed Fisale, a new AI framework for modeling complex fluid-solid interactions using neural networks inspired by classical Arbitrary Lagrangian-Eulerian methods. The system addresses limitations in existing deep learning approaches by enabling two-way interactions between fluids and solids with unified geometry-aware embeddings.
Key Takeaways
- →Fisale introduces a data-driven framework for complex two-way fluid-solid interaction problems using neural networks.
- →The system uses multiscale latent ALE grids to provide unified embeddings across different physical domains.
- →A partitioned coupling module decomposes FSI problems into structured substeps for progressive modeling.
- →The framework demonstrates superior performance in 2D and 3D FSI scenarios compared to existing models.
- →Code is publicly available on GitHub for research and development purposes.
#machine-learning#neural-networks#fluid-dynamics#computational-physics#research#arxiv#simulation#framework
Read Original →via arXiv – CS AI
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