Learning Field Reconstruction from Incomplete Data by Globally Correcting Local Estimates

Excerpt

arXiv:2610.05375v1 Announce Type: new Abstract: 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 aver