Flow Field Reconstruction with Sensor Placement Policy Learning
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913126488735744 |
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| author | Li, Ruoyan Wan, Guancheng Huang, Zijie Liu, Zixiao Wang, Haixin Luo, Xiao Wang, Wei Sun, Yizhou |
| author_facet | Li, Ruoyan Wan, Guancheng Huang, Zijie Liu, Zixiao Wang, Haixin Luo, Xiao Wang, Wei Sun, Yizhou |
| contents | Flow-field reconstruction from sparse sensor measurements remains a central challenge in modern fluid dynamics, as the need for high-fidelity data often conflicts with practical limits on sensor deployment. Existing deep learning-based methods have demonstrated promising results, but they typically depend on simplifying assumptions such as two-dimensional domains, predefined governing equations, synthetic datasets derived from idealized flow physics, and unconstrained sensor placement. In this work, we address these limitations by studying flow reconstruction under realistic conditions and introducing a directional transport-aware Graph Neural Network (GNN) that explicitly encodes both flow directionality and information transport. We further show that conventional sensor placement strategies frequently yield suboptimal configurations. To overcome this, we propose a novel Two-Step Constrained PPO procedure for Proximal Policy Optimization (PPO), which jointly optimizes sensor layouts by incorporating flow variability and accounts for reconstruction model's performance disparity with respect to sensor placement. We conduct comprehensive experiments under realistic assumptions to benchmark the performance of our reconstruction model and sensor placement policy. Together, they achieve significant improvements over existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14137 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Flow Field Reconstruction with Sensor Placement Policy Learning Li, Ruoyan Wan, Guancheng Huang, Zijie Liu, Zixiao Wang, Haixin Luo, Xiao Wang, Wei Sun, Yizhou Computational Engineering, Finance, and Science Flow-field reconstruction from sparse sensor measurements remains a central challenge in modern fluid dynamics, as the need for high-fidelity data often conflicts with practical limits on sensor deployment. Existing deep learning-based methods have demonstrated promising results, but they typically depend on simplifying assumptions such as two-dimensional domains, predefined governing equations, synthetic datasets derived from idealized flow physics, and unconstrained sensor placement. In this work, we address these limitations by studying flow reconstruction under realistic conditions and introducing a directional transport-aware Graph Neural Network (GNN) that explicitly encodes both flow directionality and information transport. We further show that conventional sensor placement strategies frequently yield suboptimal configurations. To overcome this, we propose a novel Two-Step Constrained PPO procedure for Proximal Policy Optimization (PPO), which jointly optimizes sensor layouts by incorporating flow variability and accounts for reconstruction model's performance disparity with respect to sensor placement. We conduct comprehensive experiments under realistic assumptions to benchmark the performance of our reconstruction model and sensor placement policy. Together, they achieve significant improvements over existing methods. |
| title | Flow Field Reconstruction with Sensor Placement Policy Learning |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2605.14137 |