{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CW66WEIPBVSOA3QKYABRLTBRLM","short_pith_number":"pith:CW66WEIP","schema_version":"1.0","canonical_sha256":"15bdeb110f0d64e06e0ac00315cc315b04de44f14c4996a2f7e9d8385e3e3d0b","source":{"kind":"arxiv","id":"2211.07209","version":1},"attestation_state":"computed","paper":{"title":"Learning Neural Optimal Interpolation Models and Solvers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Hugo Georgenthum, Joseph Thompson, Maxime Beauchamp, Quentin Febvre, Ronan Fablet","submitted_at":"2022-11-14T08:57:25Z","abstract_excerpt":"The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations. Optimal interpolation (OI), also referred to as kriging, provides a theoretical framework to solve interpolation problems for Gaussian processes (GP). The associated computational complexity being rapidly intractable for n-dimensional tensors and increasing numbers of observations, a rich literature has emerged to"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2211.07209","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-11-14T08:57:25Z","cross_cats_sorted":[],"title_canon_sha256":"a37b6edaf8de10ee3ffc4eb3a48e86e1733709fa9cb36ab57289f45bfa4b1f34","abstract_canon_sha256":"dd40d7a090bc8d0b01ae784c2b59f9100fc073d2895d8f6eb27bc96ef00f7d31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:50.982251Z","signature_b64":"PufmwSKDM2g7nQbSwTa0TV111g/v5gaX6uOhBtQE5bDz71xprHoFhCwJQzqiaCMVbzi8Prs/MYC7RnB7z7keAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15bdeb110f0d64e06e0ac00315cc315b04de44f14c4996a2f7e9d8385e3e3d0b","last_reissued_at":"2026-07-05T05:15:50.981766Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:50.981766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Neural Optimal Interpolation Models and Solvers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Hugo Georgenthum, Joseph Thompson, Maxime Beauchamp, Quentin Febvre, Ronan Fablet","submitted_at":"2022-11-14T08:57:25Z","abstract_excerpt":"The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations. Optimal interpolation (OI), also referred to as kriging, provides a theoretical framework to solve interpolation problems for Gaussian processes (GP). The associated computational complexity being rapidly intractable for n-dimensional tensors and increasing numbers of observations, a rich literature has emerged to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07209","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.07209/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.07209","created_at":"2026-07-05T05:15:50.981829+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.07209v1","created_at":"2026-07-05T05:15:50.981829+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07209","created_at":"2026-07-05T05:15:50.981829+00:00"},{"alias_kind":"pith_short_12","alias_value":"CW66WEIPBVSO","created_at":"2026-07-05T05:15:50.981829+00:00"},{"alias_kind":"pith_short_16","alias_value":"CW66WEIPBVSOA3QK","created_at":"2026-07-05T05:15:50.981829+00:00"},{"alias_kind":"pith_short_8","alias_value":"CW66WEIP","created_at":"2026-07-05T05:15:50.981829+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM","json":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM.json","graph_json":"https://pith.science/api/pith-number/CW66WEIPBVSOA3QKYABRLTBRLM/graph.json","events_json":"https://pith.science/api/pith-number/CW66WEIPBVSOA3QKYABRLTBRLM/events.json","paper":"https://pith.science/paper/CW66WEIP"},"agent_actions":{"view_html":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM","download_json":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM.json","view_paper":"https://pith.science/paper/CW66WEIP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.07209&json=true","fetch_graph":"https://pith.science/api/pith-number/CW66WEIPBVSOA3QKYABRLTBRLM/graph.json","fetch_events":"https://pith.science/api/pith-number/CW66WEIPBVSOA3QKYABRLTBRLM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM/action/storage_attestation","attest_author":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM/action/author_attestation","sign_citation":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM/action/citation_signature","submit_replication":"https://pith.science/pith/CW66WEIPBVSOA3QKYABRLTBRLM/action/replication_record"}},"created_at":"2026-07-05T05:15:50.981829+00:00","updated_at":"2026-07-05T05:15:50.981829+00:00"}