{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A22T55SB3YUDUBDWA4G345P4U4","short_pith_number":"pith:A22T55SB","schema_version":"1.0","canonical_sha256":"06b53ef641de283a0476070dbe75fca71b076b0d8817430d361746e3e3208ebc","source":{"kind":"arxiv","id":"2607.28104","version":1},"attestation_state":"computed","paper":{"title":"An Adaptive Finite Element Method for Marker-Driven Level-Set Transport on Hierarchical Meshes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Andrea Chierici, Eugenio Aulisa, Giacomo Barbi, Samuele Baldini, Sandro Manservisi","submitted_at":"2026-07-30T12:12:42Z","abstract_excerpt":"This work presents a new adaptive finite-element framework for level-set transport, achieving high accuracy in kinematic interface transport problems relevant to interface-capturing methods for two-phase flows while reducing computational cost. This framework accommodates dynamic refinement and coarsening, and is compatible with standard finite-element data structures. The algorithm is applicable to both structured and unstructured discretizations in two and three dimensions. The method uses a tree-based hierarchical mesh with dynamic local refinement that tracks the evolving interface, concen"},"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":"2607.28104","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-07-30T12:12:42Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"072c196ed1ec3b0dcc51b35711de8cc5eca9d4974c863c3aeca51fb430597639","abstract_canon_sha256":"bbc044c8c29a7eeac08cb628695f5774d43d8795e73fbe235604d7c63370e606"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06b53ef641de283a0476070dbe75fca71b076b0d8817430d361746e3e3208ebc","last_reissued_at":"2026-07-31T01:35:46.422578Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-31T01:35:46.422578Z"},"graph_snapshot":{"paper":{"title":"An Adaptive Finite Element Method for Marker-Driven Level-Set Transport on Hierarchical Meshes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Andrea Chierici, Eugenio Aulisa, Giacomo Barbi, Samuele Baldini, Sandro Manservisi","submitted_at":"2026-07-30T12:12:42Z","abstract_excerpt":"This work presents a new adaptive finite-element framework for level-set transport, achieving high accuracy in kinematic interface transport problems relevant to interface-capturing methods for two-phase flows while reducing computational cost. This framework accommodates dynamic refinement and coarsening, and is compatible with standard finite-element data structures. The algorithm is applicable to both structured and unstructured discretizations in two and three dimensions. The method uses a tree-based hierarchical mesh with dynamic local refinement that tracks the evolving interface, concen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28104","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/2607.28104/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":"2607.28104","created_at":"2026-07-31T01:35:46.425645+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28104v1","created_at":"2026-07-31T01:35:46.425645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28104","created_at":"2026-07-31T01:35:46.425645+00:00"},{"alias_kind":"pith_short_12","alias_value":"A22T55SB3YUD","created_at":"2026-07-31T01:35:46.425645+00:00"},{"alias_kind":"pith_short_16","alias_value":"A22T55SB3YUDUBDW","created_at":"2026-07-31T01:35:46.425645+00:00"},{"alias_kind":"pith_short_8","alias_value":"A22T55SB","created_at":"2026-07-31T01:35:46.425645+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/A22T55SB3YUDUBDWA4G345P4U4","json":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4.json","graph_json":"https://pith.science/api/pith-number/A22T55SB3YUDUBDWA4G345P4U4/graph.json","events_json":"https://pith.science/api/pith-number/A22T55SB3YUDUBDWA4G345P4U4/events.json","paper":"https://pith.science/paper/A22T55SB"},"agent_actions":{"view_html":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4","download_json":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4.json","view_paper":"https://pith.science/paper/A22T55SB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28104&json=true","fetch_graph":"https://pith.science/api/pith-number/A22T55SB3YUDUBDWA4G345P4U4/graph.json","fetch_events":"https://pith.science/api/pith-number/A22T55SB3YUDUBDWA4G345P4U4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4/action/storage_attestation","attest_author":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4/action/author_attestation","sign_citation":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4/action/citation_signature","submit_replication":"https://pith.science/pith/A22T55SB3YUDUBDWA4G345P4U4/action/replication_record"}},"created_at":"2026-07-31T01:35:46.425645+00:00","updated_at":"2026-07-31T01:35:46.425645+00:00"}