{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OHLTU4LK7HMSJFIUSDCK2A4I3B","short_pith_number":"pith:OHLTU4LK","schema_version":"1.0","canonical_sha256":"71d73a716af9d924951490c4ad0388d84ba31126b99697a88c990eb6665dcd97","source":{"kind":"arxiv","id":"2303.13056","version":2},"attestation_state":"computed","paper":{"title":"Predicting the Initial Conditions of the Universe using a Deterministic Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"astro-ph.CO","authors_text":"Aarti Singh, Albert Liang, Drew Jamieson, Shirley Ho, Vaibhav Jindal","submitted_at":"2023-03-23T06:04:36Z","abstract_excerpt":"Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Recently, deep learning has emerged as a surrogate for N-body simulations by directly learning the mapping between the linear input of an N-body simulation and the final nonlinear output from the simulation, significantly accelerating the forward modeling. However, this still does not reduce the search space for"},"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":"2303.13056","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"astro-ph.CO","submitted_at":"2023-03-23T06:04:36Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a08ea5aa643465675151b532d8816ebd61b76b42fe09f32b9816e8a7c593b55b","abstract_canon_sha256":"c757e8d7ebb91d7a577e79ce69a70ceb204067e04916b35d9feb511f0eeb3d4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:56.702670Z","signature_b64":"1oFJdfzFsZx58RNbqsP/94rUcsZY/Umc2dk3muliGlWUy6a9t6sZl/AJHE0wA5mmaxsIGzgQoy39gbLALEenDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71d73a716af9d924951490c4ad0388d84ba31126b99697a88c990eb6665dcd97","last_reissued_at":"2026-07-05T07:23:56.702170Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:56.702170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting the Initial Conditions of the Universe using a Deterministic Neural Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"astro-ph.CO","authors_text":"Aarti Singh, Albert Liang, Drew Jamieson, Shirley Ho, Vaibhav Jindal","submitted_at":"2023-03-23T06:04:36Z","abstract_excerpt":"Finding the initial conditions that led to the current state of the universe is challenging because it involves searching over an intractable input space of initial conditions, along with modeling their evolution via tools such as N-body simulations which are computationally expensive. Recently, deep learning has emerged as a surrogate for N-body simulations by directly learning the mapping between the linear input of an N-body simulation and the final nonlinear output from the simulation, significantly accelerating the forward modeling. However, this still does not reduce the search space for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13056","kind":"arxiv","version":2},"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/2303.13056/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":"2303.13056","created_at":"2026-07-05T07:23:56.702231+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.13056v2","created_at":"2026-07-05T07:23:56.702231+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13056","created_at":"2026-07-05T07:23:56.702231+00:00"},{"alias_kind":"pith_short_12","alias_value":"OHLTU4LK7HMS","created_at":"2026-07-05T07:23:56.702231+00:00"},{"alias_kind":"pith_short_16","alias_value":"OHLTU4LK7HMSJFIU","created_at":"2026-07-05T07:23:56.702231+00:00"},{"alias_kind":"pith_short_8","alias_value":"OHLTU4LK","created_at":"2026-07-05T07:23:56.702231+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10023","citing_title":"Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25385","citing_title":"On the Relation Between Field-Level Posteriors, Correlators, and their Likelihoods","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B","json":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B.json","graph_json":"https://pith.science/api/pith-number/OHLTU4LK7HMSJFIUSDCK2A4I3B/graph.json","events_json":"https://pith.science/api/pith-number/OHLTU4LK7HMSJFIUSDCK2A4I3B/events.json","paper":"https://pith.science/paper/OHLTU4LK"},"agent_actions":{"view_html":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B","download_json":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B.json","view_paper":"https://pith.science/paper/OHLTU4LK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.13056&json=true","fetch_graph":"https://pith.science/api/pith-number/OHLTU4LK7HMSJFIUSDCK2A4I3B/graph.json","fetch_events":"https://pith.science/api/pith-number/OHLTU4LK7HMSJFIUSDCK2A4I3B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B/action/storage_attestation","attest_author":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B/action/author_attestation","sign_citation":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B/action/citation_signature","submit_replication":"https://pith.science/pith/OHLTU4LK7HMSJFIUSDCK2A4I3B/action/replication_record"}},"created_at":"2026-07-05T07:23:56.702231+00:00","updated_at":"2026-07-05T07:23:56.702231+00:00"}