{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OO42SFC7FLZ7CUHOJTVHA6JNBB","short_pith_number":"pith:OO42SFC7","schema_version":"1.0","canonical_sha256":"73b9a9145f2af3f150ee4cea70792d0843a652affc6b346e88d6fb5e82c256e9","source":{"kind":"arxiv","id":"2504.19338","version":1},"attestation_state":"computed","paper":{"title":"OpenFOAMGPT 2.0: end-to-end, trustworthy automation for computational fluid dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Jingsen Feng, Ran Xu, Xu Chu","submitted_at":"2025-04-27T19:36:31Z","abstract_excerpt":"We propose the first multi agent framework for computational fluid dynamics that enables fully automated, end to end simulations directly from natural language queries. The approach integrates four specialized agents Pre processing, Prompt Generation, OpenFOAMGPT (simulator), and Post processing decomposing complex computational fluid dynamics workflows into collaborative components powered by large language models. Extensive validation through diverse case studies, including Poiseuille flows, single and multi phase porous media flows, and aerodynamic analyses, demonstrates 100% success and re"},"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":"2504.19338","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2025-04-27T19:36:31Z","cross_cats_sorted":[],"title_canon_sha256":"069c1bd1f9aff5b861f5a1b862c169c8286785a599a7d7eb3b9df43bc17f6354","abstract_canon_sha256":"d3462b564ff28b8370f0b21c28e8606c0a721e8af762353220a5dc81cf8862ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:55.926564Z","signature_b64":"irzCcng6Yiwy3g9rR93vQ0Sa13uMAwvhhktPvE1p1yJhhxroqIKQMDi9bYmF4YNTBNKmJFKOR7RFyughvP4gDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73b9a9145f2af3f150ee4cea70792d0843a652affc6b346e88d6fb5e82c256e9","last_reissued_at":"2026-07-05T10:54:55.926069Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:55.926069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OpenFOAMGPT 2.0: end-to-end, trustworthy automation for computational fluid dynamics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Jingsen Feng, Ran Xu, Xu Chu","submitted_at":"2025-04-27T19:36:31Z","abstract_excerpt":"We propose the first multi agent framework for computational fluid dynamics that enables fully automated, end to end simulations directly from natural language queries. The approach integrates four specialized agents Pre processing, Prompt Generation, OpenFOAMGPT (simulator), and Post processing decomposing complex computational fluid dynamics workflows into collaborative components powered by large language models. Extensive validation through diverse case studies, including Poiseuille flows, single and multi phase porous media flows, and aerodynamic analyses, demonstrates 100% success and re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19338","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/2504.19338/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":"2504.19338","created_at":"2026-07-05T10:54:55.926126+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.19338v1","created_at":"2026-07-05T10:54:55.926126+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19338","created_at":"2026-07-05T10:54:55.926126+00:00"},{"alias_kind":"pith_short_12","alias_value":"OO42SFC7FLZ7","created_at":"2026-07-05T10:54:55.926126+00:00"},{"alias_kind":"pith_short_16","alias_value":"OO42SFC7FLZ7CUHO","created_at":"2026-07-05T10:54:55.926126+00:00"},{"alias_kind":"pith_short_8","alias_value":"OO42SFC7","created_at":"2026-07-05T10:54:55.926126+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07850","citing_title":"PDE-Agents: An LLM-Orchestrated Multi-Agent Framework for Automated Finite Element Simulations with Knowledge Graph-Augmented Reasoning","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2502.03916","citing_title":"Experiments with Large Language Models on Retrieval-Augmented Generation for Closed-Source Simulation Software","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2603.21011","citing_title":"ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB","json":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB.json","graph_json":"https://pith.science/api/pith-number/OO42SFC7FLZ7CUHOJTVHA6JNBB/graph.json","events_json":"https://pith.science/api/pith-number/OO42SFC7FLZ7CUHOJTVHA6JNBB/events.json","paper":"https://pith.science/paper/OO42SFC7"},"agent_actions":{"view_html":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB","download_json":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB.json","view_paper":"https://pith.science/paper/OO42SFC7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.19338&json=true","fetch_graph":"https://pith.science/api/pith-number/OO42SFC7FLZ7CUHOJTVHA6JNBB/graph.json","fetch_events":"https://pith.science/api/pith-number/OO42SFC7FLZ7CUHOJTVHA6JNBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB/action/storage_attestation","attest_author":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB/action/author_attestation","sign_citation":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB/action/citation_signature","submit_replication":"https://pith.science/pith/OO42SFC7FLZ7CUHOJTVHA6JNBB/action/replication_record"}},"created_at":"2026-07-05T10:54:55.926126+00:00","updated_at":"2026-07-05T10:54:55.926126+00:00"}