{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KAKJVGPRVXYLU7RWPRCMGJWBLU","short_pith_number":"pith:KAKJVGPR","schema_version":"1.0","canonical_sha256":"50149a99f1adf0ba7e367c44c326c15d3da71a9eb7816743efeb010faf4cc96f","source":{"kind":"arxiv","id":"2205.02945","version":3},"attestation_state":"computed","paper":{"title":"Cosmology-informed neural networks to solve the background dynamics of the Universe","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["gr-qc","hep-ph"],"primary_cat":"astro-ph.CO","authors_text":"Augusto T. Chantada, Cecilia Garraffo, Claudia G. Sc\\'occola, Pavlos Protopapas, Susana J. Landau","submitted_at":"2022-05-05T21:56:43Z","abstract_excerpt":"The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neur"},"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":"2205.02945","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.CO","submitted_at":"2022-05-05T21:56:43Z","cross_cats_sorted":["gr-qc","hep-ph"],"title_canon_sha256":"0a7067caec846c6c4d1dc6673ada5a88df33fda8dc6692227299a170e8a0bc18","abstract_canon_sha256":"d08f556b53818aab31d1a261c4a2d3e40e4ad1732b4a08a9b5dcb278872e98c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:52:19.888251Z","signature_b64":"20TLYog5qoXL98Vq/3VNqhkZJ+gZ9TL7dvTse/YVQVnpnyAcjpVFqSYOhsCQp6ebrrCzwTuTHPtCb5So/SAVDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50149a99f1adf0ba7e367c44c326c15d3da71a9eb7816743efeb010faf4cc96f","last_reissued_at":"2026-07-05T05:52:19.887743Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:52:19.887743Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cosmology-informed neural networks to solve the background dynamics of the Universe","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["gr-qc","hep-ph"],"primary_cat":"astro-ph.CO","authors_text":"Augusto T. Chantada, Cecilia Garraffo, Claudia G. Sc\\'occola, Pavlos Protopapas, Susana J. Landau","submitted_at":"2022-05-05T21:56:43Z","abstract_excerpt":"The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.02945","kind":"arxiv","version":3},"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/2205.02945/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":"2205.02945","created_at":"2026-07-05T05:52:19.887805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.02945v3","created_at":"2026-07-05T05:52:19.887805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.02945","created_at":"2026-07-05T05:52:19.887805+00:00"},{"alias_kind":"pith_short_12","alias_value":"KAKJVGPRVXYL","created_at":"2026-07-05T05:52:19.887805+00:00"},{"alias_kind":"pith_short_16","alias_value":"KAKJVGPRVXYLU7RW","created_at":"2026-07-05T05:52:19.887805+00:00"},{"alias_kind":"pith_short_8","alias_value":"KAKJVGPR","created_at":"2026-07-05T05:52:19.887805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06959","citing_title":"KAN-LSTM-Transformer Neural Networks, MFV and Cosmological Parameters","ref_index":72,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU","json":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU.json","graph_json":"https://pith.science/api/pith-number/KAKJVGPRVXYLU7RWPRCMGJWBLU/graph.json","events_json":"https://pith.science/api/pith-number/KAKJVGPRVXYLU7RWPRCMGJWBLU/events.json","paper":"https://pith.science/paper/KAKJVGPR"},"agent_actions":{"view_html":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU","download_json":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU.json","view_paper":"https://pith.science/paper/KAKJVGPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.02945&json=true","fetch_graph":"https://pith.science/api/pith-number/KAKJVGPRVXYLU7RWPRCMGJWBLU/graph.json","fetch_events":"https://pith.science/api/pith-number/KAKJVGPRVXYLU7RWPRCMGJWBLU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU/action/storage_attestation","attest_author":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU/action/author_attestation","sign_citation":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU/action/citation_signature","submit_replication":"https://pith.science/pith/KAKJVGPRVXYLU7RWPRCMGJWBLU/action/replication_record"}},"created_at":"2026-07-05T05:52:19.887805+00:00","updated_at":"2026-07-05T05:52:19.887805+00:00"}