{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5LBDZAGWETFULRYCYDLXO25J2V","short_pith_number":"pith:5LBDZAGW","schema_version":"1.0","canonical_sha256":"eac23c80d624cb45c702c0d7776ba9d577a9288bb1f15125a2ca03a3a334bf1b","source":{"kind":"arxiv","id":"2506.17680","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.AI"],"primary_cat":"cs.LG","authors_text":"Qixin Liu, Rui Yang, Weijian Han, Zhengni Yang","submitted_at":"2025-06-21T11:14:54Z","abstract_excerpt":"This paper introduces a novel deep-learning approach to predict true stress-strain curves of high-strength steels from small punch test (SPT) load-displacement data. The proposed approach uses Gramian Angular Field (GAF) to transform load-displacement sequences into images, capturing spatial-temporal features and employs a Sequence-to-Sequence (Seq2Seq) model with an LSTM-based encoder-decoder architecture, enhanced by multi-head cross-attention to improved accuracy. Experimental results demonstrate that the proposed approach achieves superior prediction accuracy, with minimum and maximum mean"},"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":"2506.17680","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2025-06-21T11:14:54Z","cross_cats_sorted":["cond-mat.mtrl-sci","cs.AI"],"title_canon_sha256":"33a13ff8a20656ef7314698744b194cf304021b08340814b5ee04f7af2d781da","abstract_canon_sha256":"79cb4e97d9ad71c5eb5573deae9f9f386f60675ef8c41e7d0b65172988444cfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:25:21.693699Z","signature_b64":"cpX15gQAtfLd7/uAspSnqtVUkP4HKtygIUb3FcrQT3FwxFzflaBLQhRuVatu/RfjKfGR94tmFJmF+I/alcCSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eac23c80d624cb45c702c0d7776ba9d577a9288bb1f15125a2ca03a3a334bf1b","last_reissued_at":"2026-07-05T11:25:21.692937Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:25:21.692937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Stress-Strain Predictions with Seq2Seq and Cross-Attention based on Small Punch Test","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cond-mat.mtrl-sci","cs.AI"],"primary_cat":"cs.LG","authors_text":"Qixin Liu, Rui Yang, Weijian Han, Zhengni Yang","submitted_at":"2025-06-21T11:14:54Z","abstract_excerpt":"This paper introduces a novel deep-learning approach to predict true stress-strain curves of high-strength steels from small punch test (SPT) load-displacement data. The proposed approach uses Gramian Angular Field (GAF) to transform load-displacement sequences into images, capturing spatial-temporal features and employs a Sequence-to-Sequence (Seq2Seq) model with an LSTM-based encoder-decoder architecture, enhanced by multi-head cross-attention to improved accuracy. Experimental results demonstrate that the proposed approach achieves superior prediction accuracy, with minimum and maximum mean"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17680","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/2506.17680/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":"2506.17680","created_at":"2026-07-05T11:25:21.693024+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.17680v1","created_at":"2026-07-05T11:25:21.693024+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17680","created_at":"2026-07-05T11:25:21.693024+00:00"},{"alias_kind":"pith_short_12","alias_value":"5LBDZAGWETFU","created_at":"2026-07-05T11:25:21.693024+00:00"},{"alias_kind":"pith_short_16","alias_value":"5LBDZAGWETFULRYC","created_at":"2026-07-05T11:25:21.693024+00:00"},{"alias_kind":"pith_short_8","alias_value":"5LBDZAGW","created_at":"2026-07-05T11:25:21.693024+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/5LBDZAGWETFULRYCYDLXO25J2V","json":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V.json","graph_json":"https://pith.science/api/pith-number/5LBDZAGWETFULRYCYDLXO25J2V/graph.json","events_json":"https://pith.science/api/pith-number/5LBDZAGWETFULRYCYDLXO25J2V/events.json","paper":"https://pith.science/paper/5LBDZAGW"},"agent_actions":{"view_html":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V","download_json":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V.json","view_paper":"https://pith.science/paper/5LBDZAGW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.17680&json=true","fetch_graph":"https://pith.science/api/pith-number/5LBDZAGWETFULRYCYDLXO25J2V/graph.json","fetch_events":"https://pith.science/api/pith-number/5LBDZAGWETFULRYCYDLXO25J2V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V/action/storage_attestation","attest_author":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V/action/author_attestation","sign_citation":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V/action/citation_signature","submit_replication":"https://pith.science/pith/5LBDZAGWETFULRYCYDLXO25J2V/action/replication_record"}},"created_at":"2026-07-05T11:25:21.693024+00:00","updated_at":"2026-07-05T11:25:21.693024+00:00"}