{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:K65HKC3ZWIRELOKBTUDWSUFWMC","short_pith_number":"pith:K65HKC3Z","schema_version":"1.0","canonical_sha256":"57ba750b79b22245b9419d076950b660b34209fb120de891f9e5dc1fdf841bdc","source":{"kind":"arxiv","id":"2403.18362","version":1},"attestation_state":"computed","paper":{"title":"Fractional variational integrators based on convolution quadrature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Fernando Jim\\'enez, Khaled Hariz, Sina Ober-Bl\\\"obaum","submitted_at":"2024-03-27T08:55:08Z","abstract_excerpt":"Fractional dissipation is a powerful tool to study non-local physical phenomena such as damping models. The design of geometric, in particular, variational integrators for the numerical simulation of such systems relies on a variational formulation of the model. In [19], a new approach is proposed to deal with dissipative systems including fractionally damped systems in a variational way for both, the continuous and discrete setting. It is based on the doubling of variables and their fractional derivatives. The aim of this work is to derive higher-order fractional variational integrators by me"},"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":"2403.18362","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2024-03-27T08:55:08Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"7a57b48a3986bcbb7bd89df813e3372f3d7747a9e2379e6b98aa09b293233f6e","abstract_canon_sha256":"33f6b4e2de0d93b317537b99ba0fa54bb9b7b65d451250e9a110b16dc282d0b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:21.692510Z","signature_b64":"y05A4yq9n0EcCbUjY1k9RYcQFh76IhrWiu26MWXn+7e/41PqfCrJyqMbJpIPCGaqGnvojFWbQVeKUWCwZsaTAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57ba750b79b22245b9419d076950b660b34209fb120de891f9e5dc1fdf841bdc","last_reissued_at":"2026-07-05T08:01:21.692103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:21.692103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fractional variational integrators based on convolution quadrature","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Fernando Jim\\'enez, Khaled Hariz, Sina Ober-Bl\\\"obaum","submitted_at":"2024-03-27T08:55:08Z","abstract_excerpt":"Fractional dissipation is a powerful tool to study non-local physical phenomena such as damping models. The design of geometric, in particular, variational integrators for the numerical simulation of such systems relies on a variational formulation of the model. In [19], a new approach is proposed to deal with dissipative systems including fractionally damped systems in a variational way for both, the continuous and discrete setting. It is based on the doubling of variables and their fractional derivatives. The aim of this work is to derive higher-order fractional variational integrators by me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18362","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/2403.18362/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":"2403.18362","created_at":"2026-07-05T08:01:21.692163+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.18362v1","created_at":"2026-07-05T08:01:21.692163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18362","created_at":"2026-07-05T08:01:21.692163+00:00"},{"alias_kind":"pith_short_12","alias_value":"K65HKC3ZWIRE","created_at":"2026-07-05T08:01:21.692163+00:00"},{"alias_kind":"pith_short_16","alias_value":"K65HKC3ZWIRELOKB","created_at":"2026-07-05T08:01:21.692163+00:00"},{"alias_kind":"pith_short_8","alias_value":"K65HKC3Z","created_at":"2026-07-05T08:01:21.692163+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.20482","citing_title":"Group Relative Knowledge Distillation: Learning from Teacher's Relational Inductive Bias","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC","json":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC.json","graph_json":"https://pith.science/api/pith-number/K65HKC3ZWIRELOKBTUDWSUFWMC/graph.json","events_json":"https://pith.science/api/pith-number/K65HKC3ZWIRELOKBTUDWSUFWMC/events.json","paper":"https://pith.science/paper/K65HKC3Z"},"agent_actions":{"view_html":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC","download_json":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC.json","view_paper":"https://pith.science/paper/K65HKC3Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.18362&json=true","fetch_graph":"https://pith.science/api/pith-number/K65HKC3ZWIRELOKBTUDWSUFWMC/graph.json","fetch_events":"https://pith.science/api/pith-number/K65HKC3ZWIRELOKBTUDWSUFWMC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC/action/storage_attestation","attest_author":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC/action/author_attestation","sign_citation":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC/action/citation_signature","submit_replication":"https://pith.science/pith/K65HKC3ZWIRELOKBTUDWSUFWMC/action/replication_record"}},"created_at":"2026-07-05T08:01:21.692163+00:00","updated_at":"2026-07-05T08:01:21.692163+00:00"}