{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FRH3NRM63PCXVOTYI3I2GFN6HQ","short_pith_number":"pith:FRH3NRM6","schema_version":"1.0","canonical_sha256":"2c4fb6c59edbc57aba7846d1a315be3c0d3d092fa49dc071bcdb7a0bbe7cf416","source":{"kind":"arxiv","id":"2412.13043","version":1},"attestation_state":"computed","paper":{"title":"DiFfRG: A Discretisation Framework for functional Renormalisation Group flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.stat-mech","hep-th","physics.comp-ph","physics.flu-dyn"],"primary_cat":"hep-ph","authors_text":"Franz R. Sattler, Jan M. Pawlowski","submitted_at":"2024-12-17T16:08:06Z","abstract_excerpt":"We introduce DiFfRG (Discretisation Framework for functional Renormalisation Group flows), a comprehensive computational C++ framework for solving functional Renormalisation Group flows in very general truncation schemes. Its central features are threefold: Firstly, the use of Finite Element Methods (FEM) for efficient, easy to set up and quantitatively reliable computations of field dependences. Secondly, the (simultaneous) setup of large, fully momentum-dependent vertex expansions. Thirdly, efficient time-discretisation methods, incorporating insights from studies of solving theories which e"},"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":"2412.13043","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"hep-ph","submitted_at":"2024-12-17T16:08:06Z","cross_cats_sorted":["cond-mat.stat-mech","hep-th","physics.comp-ph","physics.flu-dyn"],"title_canon_sha256":"c63f1c08368b18603c186dd650e87eb3c28caf7535e3ab06574db59bb60ef62b","abstract_canon_sha256":"ebc3926d426e0d6d3e28d9d598908b764fddb3db794e7edb363aaa12f3dfd77b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:39.514484Z","signature_b64":"j2jFnDdjW5ZIuox++aotmXj85pkPEIUTXPK+06UiiHAVEca7O6/JWr5YMXcjTu3C5fdP+75ERkPtPN+g+GXSCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c4fb6c59edbc57aba7846d1a315be3c0d3d092fa49dc071bcdb7a0bbe7cf416","last_reissued_at":"2026-07-05T09:50:39.514014Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:39.514014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiFfRG: A Discretisation Framework for functional Renormalisation Group flows","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.stat-mech","hep-th","physics.comp-ph","physics.flu-dyn"],"primary_cat":"hep-ph","authors_text":"Franz R. Sattler, Jan M. Pawlowski","submitted_at":"2024-12-17T16:08:06Z","abstract_excerpt":"We introduce DiFfRG (Discretisation Framework for functional Renormalisation Group flows), a comprehensive computational C++ framework for solving functional Renormalisation Group flows in very general truncation schemes. Its central features are threefold: Firstly, the use of Finite Element Methods (FEM) for efficient, easy to set up and quantitatively reliable computations of field dependences. Secondly, the (simultaneous) setup of large, fully momentum-dependent vertex expansions. Thirdly, efficient time-discretisation methods, incorporating insights from studies of solving theories which e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.13043","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/2412.13043/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":"2412.13043","created_at":"2026-07-05T09:50:39.514066+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13043v1","created_at":"2026-07-05T09:50:39.514066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13043","created_at":"2026-07-05T09:50:39.514066+00:00"},{"alias_kind":"pith_short_12","alias_value":"FRH3NRM63PCX","created_at":"2026-07-05T09:50:39.514066+00:00"},{"alias_kind":"pith_short_16","alias_value":"FRH3NRM63PCXVOTY","created_at":"2026-07-05T09:50:39.514066+00:00"},{"alias_kind":"pith_short_8","alias_value":"FRH3NRM6","created_at":"2026-07-05T09:50:39.514066+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.28935","citing_title":"FunKit: A computer algebra toolkit for functional approaches","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2507.13011","citing_title":"Physics-informed operator flows and observables","ref_index":72,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ","json":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ.json","graph_json":"https://pith.science/api/pith-number/FRH3NRM63PCXVOTYI3I2GFN6HQ/graph.json","events_json":"https://pith.science/api/pith-number/FRH3NRM63PCXVOTYI3I2GFN6HQ/events.json","paper":"https://pith.science/paper/FRH3NRM6"},"agent_actions":{"view_html":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ","download_json":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ.json","view_paper":"https://pith.science/paper/FRH3NRM6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13043&json=true","fetch_graph":"https://pith.science/api/pith-number/FRH3NRM63PCXVOTYI3I2GFN6HQ/graph.json","fetch_events":"https://pith.science/api/pith-number/FRH3NRM63PCXVOTYI3I2GFN6HQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ/action/storage_attestation","attest_author":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ/action/author_attestation","sign_citation":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ/action/citation_signature","submit_replication":"https://pith.science/pith/FRH3NRM63PCXVOTYI3I2GFN6HQ/action/replication_record"}},"created_at":"2026-07-05T09:50:39.514066+00:00","updated_at":"2026-07-05T09:50:39.514066+00:00"}