发表机构
Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出TRACE框架，针对结构化感知下的物理场重构问题，通过近似贝叶斯推理、卡尔曼滤波与回溯式平滑提升重构质量，在多类物理场实验中表现优于或匹配基线方法。
AI 中文摘要
从稀疏测量数据中重构连续物理场是科学监测、逆建模及数字孪生构建的核心任务。生成式重构作为该任务的一种有前景范式，近期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。但现有方法大多假设固定的批量条件，而真实感知系统常产生结构化流：探针扫描局部区域、仪器观测移动视场，通信约束可能导致整帧缺失。本文提出TRACE，即结构化感知下物理场的回溯式流生成重构框架。TRACE在学习到的连续坐标潜在空间中执行近似贝叶斯推理，将稀疏非网格测量转化为生成式潜在证据，通过卡尔曼式滤波将其与状态空间时间先验融合，并通过回溯式平滑优化观测不足的过往帧。在活性物质、海洋声速场及超新星模拟上的实验表明，在时间稀疏、空间局部的感知协议下，TRACE的重构质量与逐帧生成式重构器、离线时空方法及流数据同化基线相当或更优。
英文摘要
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.
Comments8 pages, 4 figures, 1 table (main text); 11 figures, 15 tables in the 20-page appendix. Under review at AAAI 2027
