benchmarks evals
Sparse Sensors Do Not Always Need a Neural Winner
FlowReconBench puts ten learned and five classical reconstruction methods on common splits, sensors, calibration measures and cost accounting.
Summary
FlowReconBench puts ten learned and five classical reconstruction methods on common splits, sensors, calibration measures and cost accounting.
The leading method changes with the flow regime. Classical linear estimators lead on a cylinder wake and recover unobserved pressure; inverse-distance weighting matches the best learned methods on observed channels in large three-dimensional turbulence. Learned systems pull ahead as sensors become sparse or poorly informative. The benchmark also finds that point error and probabilistic calibration can rank models differently, while full-grid methods hit memory limits that queryable methods avoid. The paper evaluates three controlled regimes, so it supplies a comparative map rather than one universal reconstruction champion.
Why it matters
FlowReconBench puts ten learned and five classical reconstruction methods on common splits, sensors, calibration measures and cost accounting.
Limits and context
No additional limitation was separately recorded.
Key claims
FlowReconBench puts ten learned and five classical reconstruction methods on common splits, sensors, calibration measures and cost accounting.
Evidence: source-2026-11-05-006
Sources
- arXiv preprint 2611.06912, original v1arXiv · primary research
Corrections
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