PhySense: Sensor Placement Optimization for Accurate Physics Sensing

• URL: http://arxiv.org/abs/2505.18190v2
• Date: Tue, 27 May 2025 01:32:48 GMT
• Title: PhySense: Sensor Placement Optimization for Accurate Physics Sensing
• Authors: Yuezhou Ma, Haixu Wu, Hang Zhou, Huikun Weng, Jianmin Wang, Mingsheng Long,
• Abstract summary: PhySense is a framework that learns to jointly reconstruct physical fields and to optimize sensor placements.<n>It achieves state-of-the-art physics sensing accuracy and discovers informative sensor placements previously unconsidered.
• Score: 71.02216400133858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse observations and optimizing scattered sensor placements to observe maximum information. While deep learning has made rapid advances in sparse-data reconstruction, existing methods generally omit optimization of sensor placements, leaving the mutual enhancement between reconstruction and placement on the shelf. To change this suboptimal practice, we propose PhySense, a synergistic two-stage framework that learns to jointly reconstruct physical fields and to optimize sensor placements, both aiming for accurate physics sensing. The first stage involves a flow-based generative model enhanced by cross-attention to adaptively fuse sparse observations. Leveraging the reconstruction feedback, the second stage performs sensor placement via projected gradient descent to satisfy spatial constraints. We further prove that the learning objectives of the two stages are consistent with classical variance-minimization principles, providing theoretical guarantees. Extensive experiments across three challenging benchmarks, especially a 3D geometry dataset, indicate PhySense achieves state-of-the-art physics sensing accuracy and discovers informative sensor placements previously unconsidered.

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