{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:A5GZ3KCOHZ243JN3YQYPLHSUWJ","short_pith_number":"pith:A5GZ3KCO","schema_version":"1.0","canonical_sha256":"074d9da84e3e75cda5bbc430f59e54b266a123cc2639227d8919d63bae521731","source":{"kind":"arxiv","id":"2508.13867","version":1},"attestation_state":"computed","paper":{"title":"OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MS","cs.NA","math.NA","physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Adrian Kummerl\\\"ander, Martin Frank, Mathias J. Krause, Mingliang Zhong, Shota Ito, Stephan Simonis","submitted_at":"2025-08-19T14:32:10Z","abstract_excerpt":"Uncertainty quantification (UQ) is crucial in computational fluid dynamics to assess the reliability and robustness of simulations, given the uncertainties in input parameters. OpenLB is an open-source lattice Boltzmann method library designed for efficient and extensible simulations of complex fluid dynamics on high-performance computers. In this work, we leverage the efficiency of OpenLB for large-scale flow sampling with a dedicated and integrated UQ module. To this end, we focus on non-intrusive stochastic collocation methods based on generalized polynomial chaos and Monte Carlo sampling. "},"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":"2508.13867","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2025-08-19T14:32:10Z","cross_cats_sorted":["cs.MS","cs.NA","math.NA","physics.comp-ph"],"title_canon_sha256":"a080455032f950d5e4b21265499316fe147a868a7a363a1a8a2bc8b719208a24","abstract_canon_sha256":"20610c31d283090cc9d89e8f023733cd5d228751583caf58d062cb4d0bb55c91"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:08.845509Z","signature_b64":"Jr+KSIOcSI/bAsj8qBxSQpoCoFqT0QNk3gtz5+0yjQhWkIn/S9p0LKPbX4oHRrDLcTqH+AINbovND7nkeqEZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"074d9da84e3e75cda5bbc430f59e54b266a123cc2639227d8919d63bae521731","last_reissued_at":"2026-07-05T11:56:08.845068Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:08.845068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenLB-UQ: An Uncertainty Quantification Framework for Incompressible Fluid Flow Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MS","cs.NA","math.NA","physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Adrian Kummerl\\\"ander, Martin Frank, Mathias J. Krause, Mingliang Zhong, Shota Ito, Stephan Simonis","submitted_at":"2025-08-19T14:32:10Z","abstract_excerpt":"Uncertainty quantification (UQ) is crucial in computational fluid dynamics to assess the reliability and robustness of simulations, given the uncertainties in input parameters. OpenLB is an open-source lattice Boltzmann method library designed for efficient and extensible simulations of complex fluid dynamics on high-performance computers. In this work, we leverage the efficiency of OpenLB for large-scale flow sampling with a dedicated and integrated UQ module. To this end, we focus on non-intrusive stochastic collocation methods based on generalized polynomial chaos and Monte Carlo sampling. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.13867","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/2508.13867/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":"2508.13867","created_at":"2026-07-05T11:56:08.845127+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.13867v1","created_at":"2026-07-05T11:56:08.845127+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.13867","created_at":"2026-07-05T11:56:08.845127+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5GZ3KCOHZ24","created_at":"2026-07-05T11:56:08.845127+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5GZ3KCOHZ243JN3","created_at":"2026-07-05T11:56:08.845127+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5GZ3KCO","created_at":"2026-07-05T11:56:08.845127+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06144","citing_title":"Mixing of miscible liquids: Dimensionless scaling for intermediate-to-large density differences in a stirred tank","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ","json":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ.json","graph_json":"https://pith.science/api/pith-number/A5GZ3KCOHZ243JN3YQYPLHSUWJ/graph.json","events_json":"https://pith.science/api/pith-number/A5GZ3KCOHZ243JN3YQYPLHSUWJ/events.json","paper":"https://pith.science/paper/A5GZ3KCO"},"agent_actions":{"view_html":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ","download_json":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ.json","view_paper":"https://pith.science/paper/A5GZ3KCO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.13867&json=true","fetch_graph":"https://pith.science/api/pith-number/A5GZ3KCOHZ243JN3YQYPLHSUWJ/graph.json","fetch_events":"https://pith.science/api/pith-number/A5GZ3KCOHZ243JN3YQYPLHSUWJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ/action/storage_attestation","attest_author":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ/action/author_attestation","sign_citation":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ/action/citation_signature","submit_replication":"https://pith.science/pith/A5GZ3KCOHZ243JN3YQYPLHSUWJ/action/replication_record"}},"created_at":"2026-07-05T11:56:08.845127+00:00","updated_at":"2026-07-05T11:56:08.845127+00:00"}