在稀疏传感下，实时重建物理场并回溯修正过去结果。
TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing
- 用连续坐标隐空间建模，将稀疏测量转为生成证据。
- 结合卡尔曼滤波与平滑机制，提升时空重建精度。
- 适合动态监测、数字孪生等需实时更新的场景。
从稀疏测量中重构连续物理场是科学监测、反演建模和数字孪生的核心任务。生成式重建近年成为有前景的范式，通过学习数据驱动的物理先验，从有限观测中补全合理的完整场。然而，现有方法多假设固定批量条件，而真实传感系统常产生结构化流数据：探头扫描局部区域，仪器观测移动视域，通信限制可能导致整帧缺失。本文提出TRACE，一种面向结构化传感的回顾性流式生成重建框架。TRACE在学习的连续坐标隐空间中进行近似贝叶斯推断，将稀疏非网格测量转化为生成隐变量证据，通过类卡尔曼滤波融合状态空间时间先验，并利用回顾性平滑优化欠观测的历史帧。在活性物质、海洋声速场及超新星模拟数据集上的实验表明，TRACE在时空稀疏且空间局部的传感协议下，重建质量达到或超越逐帧生成重构器、离线时空方法以及流式数据同化基线。
原文摘要 · Abstract (English)
Reconstructing continuous physical fields from sparse measurements is central to scientific monitoring, inverse modeling, and digital-twin construction. Generative reconstruction has recently emerged as a promising paradigm for this task by learning data-driven physical priors that complete plausible full fields from limited observations. However, existing methods largely assume fixed, batch conditioning, whereas real sensing systems often produce structured streams: probes scan local regions, instruments observe moving fields of view, and communication constraints may leave entire frames missing. We propose TRACE, a retrospective streaming generative reconstruction framework for physical fields under structured sensing. TRACE performs approximate Bayesian inference in a learned continuous-coordinate latent space, converting sparse off-grid measurements into generative latent evidence, fusing it with a state-space temporal prior through Kalman-style filtering, and refining under-observed past frames via retrospective smoothing. Experiments on active matter, ocean sound-speed fields, and supernova simulations show that TRACE matches or surpasses frame-wise generative reconstructors, offline spatiotemporal methods, and streaming data-assimilation baselines in reconstruction quality under temporally sparse and spatially localized sensing protocols.
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