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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.07835 |
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| _version_ | 1866912187644116992 |
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| author | Wang, Yikai Wang, Jiameng Han, Ruyi Fu, Shujun |
| author_facet | Wang, Yikai Wang, Jiameng Han, Ruyi Fu, Shujun |
| contents | In this paper we present advanced representation learning study on integrating deep learning techniques and sparse approximation, including diffusion models, for advanced flow field analysis and reconstruction. Key applications include super-resolution flow field reconstruction, flow field inpainting, fluid-structure interaction, transient and internal flow analyses, and reduced-order modeling. The study introduces two novel methods: flow diffusions for super-resolution tasks and a sparsity-boosted low-rank model for flow field inpainting. By leveraging cutting-edge methodologies in computational fluid dynamics (CFD), the proposed approaches improve accuracy, computational efficiency, and adaptability, offering deeper insights into complex flow dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advanced representation learning for flow field analysis and reconstruction Wang, Yikai Wang, Jiameng Han, Ruyi Fu, Shujun Fluid Dynamics In this paper we present advanced representation learning study on integrating deep learning techniques and sparse approximation, including diffusion models, for advanced flow field analysis and reconstruction. Key applications include super-resolution flow field reconstruction, flow field inpainting, fluid-structure interaction, transient and internal flow analyses, and reduced-order modeling. The study introduces two novel methods: flow diffusions for super-resolution tasks and a sparsity-boosted low-rank model for flow field inpainting. By leveraging cutting-edge methodologies in computational fluid dynamics (CFD), the proposed approaches improve accuracy, computational efficiency, and adaptability, offering deeper insights into complex flow dynamics. |
| title | Advanced representation learning for flow field analysis and reconstruction |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2501.07835 |