{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TYH3O45FT34NYPFURNILTADBFJ","short_pith_number":"pith:TYH3O45F","schema_version":"1.0","canonical_sha256":"9e0fb773a59ef8dc3cb48b50b980612a4ac83196bd42b06be7ac4817e08c698b","source":{"kind":"arxiv","id":"2607.27062","version":1},"attestation_state":"computed","paper":{"title":"PIKS: Universal Physics-Informed Kernel Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Giacomo Meanti, Joachim Bona-Pellissier, Lorenzo Rosasco, Matteo Santacesaria","submitted_at":"2026-07-29T15:53:03Z","abstract_excerpt":"Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (PINNs) dominate empirical applications, the complexity of neural network architectures and optimization landscapes hinders the development of a corresponding learning theory. In turn, kernel methods offer an appealing alternative with closed-form solutions and analytical tractability, yet existing guarantees primarily cover the well-specified setting where the target belongs to the native Reproducing Kernel Hilbert Sp"},"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":"2607.27062","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-07-29T15:53:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a1426519dd2bc867b0fdd579ddcbe3fa68a1c39a7810c42eb050a44d87e95fd8","abstract_canon_sha256":"8339a7f897642bf194eb113791a1d32488feecaf34899d440383a5b85aa58b35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e0fb773a59ef8dc3cb48b50b980612a4ac83196bd42b06be7ac4817e08c698b","last_reissued_at":"2026-07-30T01:23:28.551384Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:23:28.551384Z"},"graph_snapshot":{"paper":{"title":"PIKS: Universal Physics-Informed Kernel Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Giacomo Meanti, Joachim Bona-Pellissier, Lorenzo Rosasco, Matteo Santacesaria","submitted_at":"2026-07-29T15:53:03Z","abstract_excerpt":"Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (PINNs) dominate empirical applications, the complexity of neural network architectures and optimization landscapes hinders the development of a corresponding learning theory. In turn, kernel methods offer an appealing alternative with closed-form solutions and analytical tractability, yet existing guarantees primarily cover the well-specified setting where the target belongs to the native Reproducing Kernel Hilbert Sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.27062","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/2607.27062/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":"2607.27062","created_at":"2026-07-30T01:23:28.556286+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.27062v1","created_at":"2026-07-30T01:23:28.556286+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.27062","created_at":"2026-07-30T01:23:28.556286+00:00"},{"alias_kind":"pith_short_12","alias_value":"TYH3O45FT34N","created_at":"2026-07-30T01:23:28.556286+00:00"},{"alias_kind":"pith_short_16","alias_value":"TYH3O45FT34NYPFU","created_at":"2026-07-30T01:23:28.556286+00:00"},{"alias_kind":"pith_short_8","alias_value":"TYH3O45F","created_at":"2026-07-30T01:23:28.556286+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/TYH3O45FT34NYPFURNILTADBFJ","json":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ.json","graph_json":"https://pith.science/api/pith-number/TYH3O45FT34NYPFURNILTADBFJ/graph.json","events_json":"https://pith.science/api/pith-number/TYH3O45FT34NYPFURNILTADBFJ/events.json","paper":"https://pith.science/paper/TYH3O45F"},"agent_actions":{"view_html":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ","download_json":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ.json","view_paper":"https://pith.science/paper/TYH3O45F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.27062&json=true","fetch_graph":"https://pith.science/api/pith-number/TYH3O45FT34NYPFURNILTADBFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/TYH3O45FT34NYPFURNILTADBFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ/action/storage_attestation","attest_author":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ/action/author_attestation","sign_citation":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ/action/citation_signature","submit_replication":"https://pith.science/pith/TYH3O45FT34NYPFURNILTADBFJ/action/replication_record"}},"created_at":"2026-07-30T01:23:28.556286+00:00","updated_at":"2026-07-30T01:23:28.556286+00:00"}