Learning Field Reconstruction from Incomplete Data by Globally Correcting Local Estimates
Reconstructing physical fields from training samples that are always incomplete requires learning spatial structure from fragmented observations. Existing context--query work establishes how held-out observations provide valid training targets, but this does not make the complete-field distribution identifiable when every training field is incomplete. With finite data, weak evidence of sharp transitions and localized variations can further favor averaged predictions that attenuate local detail. A structural prior is therefore needed to favor plausible completions; local spatial relationships offer one grounded in the observations. We propose a locally constructed, globally revisable estim...
| Authors | Renhao Zhong, Zihan Zhou, Chiyuan Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 October 2026 |
| Last updated at source | 7 October 2026 |
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