{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KNSLOZP7YNRPY3KWIRA2CQL6ZD","short_pith_number":"pith:KNSLOZP7","schema_version":"1.0","canonical_sha256":"5364b765ffc362fc6d564441a1417ec8d907eade4ff7dbc3d71df9d40ea26fb2","source":{"kind":"arxiv","id":"2602.01929","version":2},"attestation_state":"computed","paper":{"title":"Probabilistic function-on-function nonlinear autoregressive model for emulation and reliability analysis of stochastic dynamical systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"math.DS","authors_text":"Bruno Sudret, Marcos A. Valdebenito, Matthias G.R. Faes, Stefano Marelli, Styfen Sch\\\"ar, Zhouzhou Song","submitted_at":"2026-02-02T10:29:30Z","abstract_excerpt":"Constructing accurate and computationally efficient surrogate models (or emulators) for predicting dynamical system responses is critical in many engineering domains, yet remains challenging due to the strongly nonlinear and high-dimensional mapping from external excitations and system parameters to system responses. This work introduces a novel Function-on-Function Nonlinear AutoRegressive model with eXogenous inputs (F2NARX), which reformulates the recently proposed $\\mathcal{F}$-NARX method from a function-on-function regression perspective. The proposed framework substantially improves pre"},"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":"2602.01929","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math.DS","submitted_at":"2026-02-02T10:29:30Z","cross_cats_sorted":["stat.CO","stat.ML"],"title_canon_sha256":"55d98707ae88d7284f73fa26253d1ae40d530f04ecb7093364dd7cc118f34785","abstract_canon_sha256":"5746ec6bd0043739c2faf15c3a05ce53b86cd44f81f03bcfc63c75ef7408ba87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:12:50.957045Z","signature_b64":"5jNzeafFNzdRS5WDIeIz0eZQuK7GEOoPK4oVf6lsBxcIJh2JSXoCvsGp/vS3xk32gBEjqUVZbWsvLgx7x9WCDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5364b765ffc362fc6d564441a1417ec8d907eade4ff7dbc3d71df9d40ea26fb2","last_reissued_at":"2026-06-19T16:12:50.956509Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:12:50.956509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic function-on-function nonlinear autoregressive model for emulation and reliability analysis of stochastic dynamical systems","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"math.DS","authors_text":"Bruno Sudret, Marcos A. Valdebenito, Matthias G.R. Faes, Stefano Marelli, Styfen Sch\\\"ar, Zhouzhou Song","submitted_at":"2026-02-02T10:29:30Z","abstract_excerpt":"Constructing accurate and computationally efficient surrogate models (or emulators) for predicting dynamical system responses is critical in many engineering domains, yet remains challenging due to the strongly nonlinear and high-dimensional mapping from external excitations and system parameters to system responses. This work introduces a novel Function-on-Function Nonlinear AutoRegressive model with eXogenous inputs (F2NARX), which reformulates the recently proposed $\\mathcal{F}$-NARX method from a function-on-function regression perspective. The proposed framework substantially improves pre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.01929","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/2602.01929/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":"2602.01929","created_at":"2026-06-19T16:12:50.956568+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.01929v2","created_at":"2026-06-19T16:12:50.956568+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.01929","created_at":"2026-06-19T16:12:50.956568+00:00"},{"alias_kind":"pith_short_12","alias_value":"KNSLOZP7YNRP","created_at":"2026-06-19T16:12:50.956568+00:00"},{"alias_kind":"pith_short_16","alias_value":"KNSLOZP7YNRPY3KW","created_at":"2026-06-19T16:12:50.956568+00:00"},{"alias_kind":"pith_short_8","alias_value":"KNSLOZP7","created_at":"2026-06-19T16:12:50.956568+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.12248","citing_title":"Time-variant reliability using time-dependent surrogate models","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD","json":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD.json","graph_json":"https://pith.science/api/pith-number/KNSLOZP7YNRPY3KWIRA2CQL6ZD/graph.json","events_json":"https://pith.science/api/pith-number/KNSLOZP7YNRPY3KWIRA2CQL6ZD/events.json","paper":"https://pith.science/paper/KNSLOZP7"},"agent_actions":{"view_html":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD","download_json":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD.json","view_paper":"https://pith.science/paper/KNSLOZP7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.01929&json=true","fetch_graph":"https://pith.science/api/pith-number/KNSLOZP7YNRPY3KWIRA2CQL6ZD/graph.json","fetch_events":"https://pith.science/api/pith-number/KNSLOZP7YNRPY3KWIRA2CQL6ZD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD/action/storage_attestation","attest_author":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD/action/author_attestation","sign_citation":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD/action/citation_signature","submit_replication":"https://pith.science/pith/KNSLOZP7YNRPY3KWIRA2CQL6ZD/action/replication_record"}},"created_at":"2026-06-19T16:12:50.956568+00:00","updated_at":"2026-06-19T16:12:50.956568+00:00"}