MIMONet recovers sea-surface height with no sensors, cutting error 4.1x vs climatology
bravo_abad · x · 2026-09-22
Kobayashi and coauthors use neural operators as "virtual sensors": MIMONet reconstructs continuous fields from sparse measurements of different physical variables, recovering quantities that have no direct sensors at all.
The standout test is the North Atlantic: the model receives temperature at 400 locations and salinity at another 400, and must infer sea-surface height purely from relationships learned in training. It achieves 9.1% relative error versus 37.7% for climatology — a 4.1-fold reduction.
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