arXiv:2505.18190v5 Announce Type: replace-cross 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
The increasing sophistication of AI for reconstruction tasks is now being extended to optimize data collection itself, addressing a recognized bottleneck in real-world applications.
This development allows for more accurate and efficient physical sensing, crucial for numerous scientific and engineering applications, potentially reducing resource expenditure and improving model reliability.
The ability to jointly optimize sensor placement and data reconstruction fundamentally changes how physical fields are observed and modelled, moving beyond static sensor array designs.
- · AI/ML researchers
- · Sensor manufacturers
- · Robotics and automation sectors
- · Environmental monitoring agencies
- · Inefficient traditional sensor network configurators
- · Domains reliant on manual sensor deployment heuristics
Improved accuracy and reduced data acquisition costs in physics sensing across various applications.
Acceleration of research and development in fields heavily reliant on environmental or physical data, like climate modeling or materials science.
Enhanced operational autonomy and decision-making capabilities in AI-driven systems due to more precise real-time environmental awareness.
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Read at arXiv cs.LG