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. \correct{Leveraging the reconstruction feedback, }the second stage performs sensor placement via projected gradient descent to satisfy spatial constraints. \correct{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.
Abstract (translated)
物理学感知在许多科学和工程领域中扮演着核心角色，这通常涉及到两个相互关联的任务：从稀疏观测数据重建密集的物理场以及优化传感器布置以获取最大信息量。虽然深度学习在处理稀疏数据重建方面取得了快速进展，但现有方法一般忽略了传感器布局优化的问题，从而导致了重建与布局之间的协同提升被搁置一旁。为了改变这种次优做法，我们提出了一种名为PhySense的协同两阶段框架，该框架能够同时学习物理场的联合重构和传感器布置的优化，两者都旨在实现准确的物理学感知。 第一阶段涉及一种通过跨注意力增强的流生成模型，用于自适应地融合稀疏观测数据。利用重建反馈，第二阶段则采用投影梯度下降法进行传感器放置以满足空间约束条件。此外，我们还证明了两个阶段的学习目标与经典的方差最小化原理一致，并提供了理论保证。 在三个具有挑战性的基准测试（尤其是3D几何数据集）中的广泛实验表明，PhySense达到了最先进的物理学感知准确度，并且发现了之前未曾考虑的有信息量的传感器布局。
URL
https://arxiv.org/abs/2505.18190
