TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing

2026-08-26

AI Summary

本文提出TRACE框架，通过学习连续坐标潜在空间和卡尔曼滤波融合方法，解决稀疏结构化传感条件下物理场的流式生成重构问题。

Abstract

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.

Problem

• sparse measurements
• physical fields
• structured sensing
• generative reconstruction
• continuous physical fields

Innovation

• Retrospective Streaming
• Generative Reconstruction
• Bayesian Inference
• Kalman-style Filtering
• Sparse Structured Sensing