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Integrity report for Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flames

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.10028

0Critical
0Advisory
4Detectors run
2026-05-20Last checked

Paper page arXiv integrity.json

Detector runs

claim_evidence completed v1.0.0 · findings 0 · 2026-05-20 06:42:01.107291+00:00
ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-19 15:42:15.449485+00:00
doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-19 12:01:18.116258+00:00
doi_compliance completed v1.0.0 · findings 0 · 2026-05-19 09:43:03.215065+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/2605.10028/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.