{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:K7Z4FNH5LMH6Q63NOPQ3ZLRNI5","short_pith_number":"pith:K7Z4FNH5","schema_version":"1.0","canonical_sha256":"57f3c2b4fd5b0fe87b6d73e1bcae2d474419d019f675145ab57d2a498f24b606","source":{"kind":"arxiv","id":"2203.16494","version":1},"attestation_state":"computed","paper":{"title":"S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.NA"],"primary_cat":"math.NA","authors_text":"Dylan Matthew Copeland, Jessica T. Lauzon, Kevin Huynh, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi","submitted_at":"2022-03-29T05:20:17Z","abstract_excerpt":"While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce a points selection algorithm developed by Shin and Xiu [SIAM J. Sci. Comput., 38 (2016), pp. A385--A411], as a hyper-reduction method. The selection algorithm is originally proposed as a stochastic collocation "},"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":"2203.16494","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2022-03-29T05:20:17Z","cross_cats_sorted":["cs.CE","cs.NA"],"title_canon_sha256":"84ffabb5d4f31e4ef4172ac85565b1fc1a658c3d55e85758cc1f93b0f58d873a","abstract_canon_sha256":"bac0d5318219b0507378a767f7ae5813b6f062e9b309543cca2c576d9c9d4a95"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:39.863033Z","signature_b64":"GVYFFnhwQNnzPFmd3K/WNU4MqFeIxEcbS9B8PLGK3L7jYT0opUD3iuJw5dKvD7Qt7doH9vQKj6h/tQemnH0xBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57f3c2b4fd5b0fe87b6d73e1bcae2d474419d019f675145ab57d2a498f24b606","last_reissued_at":"2026-07-05T08:51:39.862606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:39.862606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"S-OPT: A Points Selection Algorithm for Hyper-Reduction in Reduced Order Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.NA"],"primary_cat":"math.NA","authors_text":"Dylan Matthew Copeland, Jessica T. Lauzon, Kevin Huynh, Siu Wun Cheung, Yeonjong Shin, Youngsoo Choi","submitted_at":"2022-03-29T05:20:17Z","abstract_excerpt":"While projection-based reduced order models can reduce the dimension of full order solutions, the resulting reduced models may still contain terms that scale with the full order dimension. Hyper-reduction techniques are sampling-based methods that further reduce this computational complexity by approximating such terms with a much smaller dimension. The goal of this work is to introduce a points selection algorithm developed by Shin and Xiu [SIAM J. Sci. Comput., 38 (2016), pp. A385--A411], as a hyper-reduction method. The selection algorithm is originally proposed as a stochastic collocation "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.16494","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/2203.16494/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":"2203.16494","created_at":"2026-07-05T08:51:39.862662+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.16494v1","created_at":"2026-07-05T08:51:39.862662+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.16494","created_at":"2026-07-05T08:51:39.862662+00:00"},{"alias_kind":"pith_short_12","alias_value":"K7Z4FNH5LMH6","created_at":"2026-07-05T08:51:39.862662+00:00"},{"alias_kind":"pith_short_16","alias_value":"K7Z4FNH5LMH6Q63N","created_at":"2026-07-05T08:51:39.862662+00:00"},{"alias_kind":"pith_short_8","alias_value":"K7Z4FNH5","created_at":"2026-07-05T08:51:39.862662+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2410.21770","citing_title":"Tensor-based empirical interpolation method and its application in model reduction","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5","json":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5.json","graph_json":"https://pith.science/api/pith-number/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/graph.json","events_json":"https://pith.science/api/pith-number/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/events.json","paper":"https://pith.science/paper/K7Z4FNH5"},"agent_actions":{"view_html":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5","download_json":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5.json","view_paper":"https://pith.science/paper/K7Z4FNH5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.16494&json=true","fetch_graph":"https://pith.science/api/pith-number/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/graph.json","fetch_events":"https://pith.science/api/pith-number/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/action/storage_attestation","attest_author":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/action/author_attestation","sign_citation":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/action/citation_signature","submit_replication":"https://pith.science/pith/K7Z4FNH5LMH6Q63NOPQ3ZLRNI5/action/replication_record"}},"created_at":"2026-07-05T08:51:39.862662+00:00","updated_at":"2026-07-05T08:51:39.862662+00:00"}