{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2009:Z6N4FHWVKR7342SCAAPWEFPRG4","short_pith_number":"pith:Z6N4FHWV","schema_version":"1.0","canonical_sha256":"cf9bc29ed5547fbe6a42001f6215f1373cd706b4712fb3ea5c5248f20012ac2b","source":{"kind":"arxiv","id":"0911.4816","version":4},"attestation_state":"computed","paper":{"title":"Dynamical modelling of superstatistical complex systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.stat-mech","authors_text":"Christian Beck, Erik Van der Straeten","submitted_at":"2009-11-25T11:16:26Z","abstract_excerpt":"We show how to construct the optimum superstatistical dynamical model for a given experimentally measured time series. For this purpose we generalise the superstatistics concept and study a Langevin equation with a memory kernel whose parameters fluctuate on a large time scale. It is shown how to construct a synthetic dynamical model with the same invariant density and correlation function as the experimental data. As a main example we apply our method to velocity time series measured in high-Reynolds number turbulent Taylor-Couette flow, but the method can be applied to many other complex sys"},"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":"0911.4816","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cond-mat.stat-mech","submitted_at":"2009-11-25T11:16:26Z","cross_cats_sorted":[],"title_canon_sha256":"c0444e9a0883d0c96eac0aa502083b030f4faf6366d6825f19d99b549a1499dd","abstract_canon_sha256":"472695f20e44f4e9adfb9f5f2ae14fd0e8d30ccfd5b3e129d156f391bb102a7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:32:00.058345Z","signature_b64":"3kSHBAd3ExBE14vSjU9ZQejg7IC2fUVlKC3n9pdAEIXsnr0Bn/9sDsjD64AkfhsY0ZHxrEWa5NyRZkjmPhx8DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf9bc29ed5547fbe6a42001f6215f1373cd706b4712fb3ea5c5248f20012ac2b","last_reissued_at":"2026-05-18T04:32:00.057716Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:32:00.057716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamical modelling of superstatistical complex systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.stat-mech","authors_text":"Christian Beck, Erik Van der Straeten","submitted_at":"2009-11-25T11:16:26Z","abstract_excerpt":"We show how to construct the optimum superstatistical dynamical model for a given experimentally measured time series. For this purpose we generalise the superstatistics concept and study a Langevin equation with a memory kernel whose parameters fluctuate on a large time scale. It is shown how to construct a synthetic dynamical model with the same invariant density and correlation function as the experimental data. As a main example we apply our method to velocity time series measured in high-Reynolds number turbulent Taylor-Couette flow, but the method can be applied to many other complex sys"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0911.4816","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"0911.4816","created_at":"2026-05-18T04:32:00.057798+00:00"},{"alias_kind":"arxiv_version","alias_value":"0911.4816v4","created_at":"2026-05-18T04:32:00.057798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0911.4816","created_at":"2026-05-18T04:32:00.057798+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z6N4FHWVKR73","created_at":"2026-05-18T12:26:02.257875+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z6N4FHWVKR7342SC","created_at":"2026-05-18T12:26:02.257875+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z6N4FHWV","created_at":"2026-05-18T12:26:02.257875+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/Z6N4FHWVKR7342SCAAPWEFPRG4","json":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4.json","graph_json":"https://pith.science/api/pith-number/Z6N4FHWVKR7342SCAAPWEFPRG4/graph.json","events_json":"https://pith.science/api/pith-number/Z6N4FHWVKR7342SCAAPWEFPRG4/events.json","paper":"https://pith.science/paper/Z6N4FHWV"},"agent_actions":{"view_html":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4","download_json":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4.json","view_paper":"https://pith.science/paper/Z6N4FHWV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0911.4816&json=true","fetch_graph":"https://pith.science/api/pith-number/Z6N4FHWVKR7342SCAAPWEFPRG4/graph.json","fetch_events":"https://pith.science/api/pith-number/Z6N4FHWVKR7342SCAAPWEFPRG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4/action/storage_attestation","attest_author":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4/action/author_attestation","sign_citation":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4/action/citation_signature","submit_replication":"https://pith.science/pith/Z6N4FHWVKR7342SCAAPWEFPRG4/action/replication_record"}},"created_at":"2026-05-18T04:32:00.057798+00:00","updated_at":"2026-05-18T04:32:00.057798+00:00"}