{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NVPRVY4B3RFTPFI7JI3E5KNVGY","short_pith_number":"pith:NVPRVY4B","schema_version":"1.0","canonical_sha256":"6d5f1ae381dc4b37951f4a364ea9b5360bb0ecfea968983121a9eea3f5eaaf7e","source":{"kind":"arxiv","id":"2607.22019","version":1},"attestation_state":"computed","paper":{"title":"PIML-OFEM: A New Large-Scale Structural Analysis Method Based on Problem-Independent Machine Learning and Overlapping Finite Element Technique","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","physics.comp-ph"],"primary_cat":"math.NA","authors_text":"Chang Liu, Changyu Shen, Jingyu Feng, Jin Liu, Tianxing Yang, Xinyang Zhang, Xu Guo, Yang Li, Yilin Guo, Zongliang Du","submitted_at":"2026-07-24T06:34:10Z","abstract_excerpt":"High-resolution analysis and design of large-scale heterogeneous structures require accurate reduced-order models and efficient online computation. Existing multiscale methods must repeatedly construct local basis functions for different material distributions, whereas substructure-based problem-independent machine learning (PIML) methods can be limited by prescribed boundary displacement interpolation. We propose PIML-OFEM, an overlapping finite element method accelerated by problem-independent machine learning. Each substructure retains only its corner-node degrees of freedom. Oversampled nu"},"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":"2607.22019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-07-24T06:34:10Z","cross_cats_sorted":["cs.NA","physics.comp-ph"],"title_canon_sha256":"1c95c723a087ca1312ed239663526e21ee5dc81c25b2ca25bb4848a49950ee8b","abstract_canon_sha256":"c7d474a342c43826e23afcef1d541d4fa43c60db3d57d1743eff4cd78527e067"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T01:20:43.510992Z","signature_b64":"laWiVvFrFAseEToFUFFXG4pzZxDPEPxe91MtpouXRSm0YV5/0nDwJ/3jzYft8FkkweKaDpPXzTgm3cEqWMbXCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d5f1ae381dc4b37951f4a364ea9b5360bb0ecfea968983121a9eea3f5eaaf7e","last_reissued_at":"2026-07-27T01:20:43.510148Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T01:20:43.510148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PIML-OFEM: A New Large-Scale Structural Analysis Method Based on Problem-Independent Machine Learning and Overlapping Finite Element Technique","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","physics.comp-ph"],"primary_cat":"math.NA","authors_text":"Chang Liu, Changyu Shen, Jingyu Feng, Jin Liu, Tianxing Yang, Xinyang Zhang, Xu Guo, Yang Li, Yilin Guo, Zongliang Du","submitted_at":"2026-07-24T06:34:10Z","abstract_excerpt":"High-resolution analysis and design of large-scale heterogeneous structures require accurate reduced-order models and efficient online computation. Existing multiscale methods must repeatedly construct local basis functions for different material distributions, whereas substructure-based problem-independent machine learning (PIML) methods can be limited by prescribed boundary displacement interpolation. We propose PIML-OFEM, an overlapping finite element method accelerated by problem-independent machine learning. Each substructure retains only its corner-node degrees of freedom. Oversampled nu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22019","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/2607.22019/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":"2607.22019","created_at":"2026-07-27T01:20:43.510594+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22019v1","created_at":"2026-07-27T01:20:43.510594+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22019","created_at":"2026-07-27T01:20:43.510594+00:00"},{"alias_kind":"pith_short_12","alias_value":"NVPRVY4B3RFT","created_at":"2026-07-27T01:20:43.510594+00:00"},{"alias_kind":"pith_short_16","alias_value":"NVPRVY4B3RFTPFI7","created_at":"2026-07-27T01:20:43.510594+00:00"},{"alias_kind":"pith_short_8","alias_value":"NVPRVY4B","created_at":"2026-07-27T01:20:43.510594+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/NVPRVY4B3RFTPFI7JI3E5KNVGY","json":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY.json","graph_json":"https://pith.science/api/pith-number/NVPRVY4B3RFTPFI7JI3E5KNVGY/graph.json","events_json":"https://pith.science/api/pith-number/NVPRVY4B3RFTPFI7JI3E5KNVGY/events.json","paper":"https://pith.science/paper/NVPRVY4B"},"agent_actions":{"view_html":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY","download_json":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY.json","view_paper":"https://pith.science/paper/NVPRVY4B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22019&json=true","fetch_graph":"https://pith.science/api/pith-number/NVPRVY4B3RFTPFI7JI3E5KNVGY/graph.json","fetch_events":"https://pith.science/api/pith-number/NVPRVY4B3RFTPFI7JI3E5KNVGY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY/action/storage_attestation","attest_author":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY/action/author_attestation","sign_citation":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY/action/citation_signature","submit_replication":"https://pith.science/pith/NVPRVY4B3RFTPFI7JI3E5KNVGY/action/replication_record"}},"created_at":"2026-07-27T01:20:43.510594+00:00","updated_at":"2026-07-27T01:20:43.510594+00:00"}