PhySense: Sensor Placement Optimization for Accurate Physics Sensing

2025-05-19

AI Summary

This work addresses sparse physical field sensing by jointly optimizing sensor placement and field reconstruction accuracy, overcoming information underutilization caused by their conventional decoupling. We propose a reconstruction-guided closed-loop sensing framework: (1) a high-fidelity field reconstruction module leveraging normalizing flows and cross-attention mechanisms; (2) a spatially constrained projected gradient descent method for co-optimizing sensor deployment, with theoretical proof of equivalence to variance minimization. Our approach is the first to discover high-information non-uniform sensor layouts, breaking from traditional uniform or heuristic placement paradigms. Evaluated on three benchmarks—including a complex 3D geometric dataset—it achieves state-of-the-art reconstruction accuracy. At equivalent reconstruction error, it reduces required sensor count by 40%, significantly enhancing sensing efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizing sensor placement for accurate physics sensing
• Reconstructing dense physical fields from sparse observations
• Jointly enhancing reconstruction and sensor placement tasks

Innovation

• Two-stage framework for joint reconstruction and placement
• Flow-based generative model with cross-attention fusion
• Projected gradient descent for constrained sensor placement