{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MYQJ2NK7I6UY4LKDPVF2EZWMYJ","short_pith_number":"pith:MYQJ2NK7","schema_version":"1.0","canonical_sha256":"66209d355f47a98e2d437d4ba266ccc2771eabbf64f1763fff79a8d0445aa72c","source":{"kind":"arxiv","id":"2501.11937","version":2},"attestation_state":"computed","paper":{"title":"MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jie Liu, Jing Xiao, Qingling Wang, Xinhai Chen","submitted_at":"2025-01-21T07:27:05Z","abstract_excerpt":"Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address this challenge, physics-informed intelligent learning methods have recently emerged, significantly improving generation efficiency while maintaining high mesh quality. However, physics-informed methods fail to generalize when applied to previously unseen geometries, as even small changes in the boundary shape necessitate burdensome retraining to adapt to new geometric variations."},"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":"2501.11937","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-21T07:27:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"001622d3d0b50c8b689cc46c28e40a45ebeaaccb7c6841ec06746b6f01b70000","abstract_canon_sha256":"55440f698f700737a3b85439ac274e315bac4e02a2049c04e20003d23ac8da09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:09:14.559722Z","signature_b64":"qUyZS+NEaSbOhyNop7aTHh4c4Yfa+Mibx/iSiQbn1Cqigk4oD06rY4YQpJm/8WqwXJ2sbkM709JI5MKosGjYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66209d355f47a98e2d437d4ba266ccc2771eabbf64f1763fff79a8d0445aa72c","last_reissued_at":"2026-06-10T01:09:14.558639Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:09:14.558639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jie Liu, Jing Xiao, Qingling Wang, Xinhai Chen","submitted_at":"2025-01-21T07:27:05Z","abstract_excerpt":"Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address this challenge, physics-informed intelligent learning methods have recently emerged, significantly improving generation efficiency while maintaining high mesh quality. However, physics-informed methods fail to generalize when applied to previously unseen geometries, as even small changes in the boundary shape necessitate burdensome retraining to adapt to new geometric variations."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.11937","kind":"arxiv","version":2},"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/2501.11937/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":"2501.11937","created_at":"2026-06-10T01:09:14.558838+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.11937v2","created_at":"2026-06-10T01:09:14.558838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.11937","created_at":"2026-06-10T01:09:14.558838+00:00"},{"alias_kind":"pith_short_12","alias_value":"MYQJ2NK7I6UY","created_at":"2026-06-10T01:09:14.558838+00:00"},{"alias_kind":"pith_short_16","alias_value":"MYQJ2NK7I6UY4LKD","created_at":"2026-06-10T01:09:14.558838+00:00"},{"alias_kind":"pith_short_8","alias_value":"MYQJ2NK7","created_at":"2026-06-10T01:09:14.558838+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/MYQJ2NK7I6UY4LKDPVF2EZWMYJ","json":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ.json","graph_json":"https://pith.science/api/pith-number/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/graph.json","events_json":"https://pith.science/api/pith-number/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/events.json","paper":"https://pith.science/paper/MYQJ2NK7"},"agent_actions":{"view_html":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ","download_json":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ.json","view_paper":"https://pith.science/paper/MYQJ2NK7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.11937&json=true","fetch_graph":"https://pith.science/api/pith-number/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/graph.json","fetch_events":"https://pith.science/api/pith-number/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/action/storage_attestation","attest_author":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/action/author_attestation","sign_citation":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/action/citation_signature","submit_replication":"https://pith.science/pith/MYQJ2NK7I6UY4LKDPVF2EZWMYJ/action/replication_record"}},"created_at":"2026-06-10T01:09:14.558838+00:00","updated_at":"2026-06-10T01:09:14.558838+00:00"}