{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CCOR5OMKSMFUGVOPMZ6EKOSNKR","short_pith_number":"pith:CCOR5OMK","schema_version":"1.0","canonical_sha256":"109d1eb98a930b4355cf667c453a4d54732fda73c3a03c101a39873e24dfebd2","source":{"kind":"arxiv","id":"2505.03806","version":2},"attestation_state":"computed","paper":{"title":"Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Marzieh Najariyan, Mehran Mazandarani","submitted_at":"2025-05-02T09:08:07Z","abstract_excerpt":"This article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a platform for the integration of diverse forms of perception precisiation, including singular, probability distribution, possibility distribution, interval, and fuzzy graph. In fact, PrINNs allow neural networks to model dynamical systems by inte"},"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":"2505.03806","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-02T09:08:07Z","cross_cats_sorted":["cs.AI","cs.NE"],"title_canon_sha256":"46d40be5d5653770afee4a65f755990130627dbc42a4140c9e7e1b109db55c3d","abstract_canon_sha256":"d5f38132eb4fa185b9cae6072619e5283f7a0682511151a8350d94e58131bcbc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:41.098123Z","signature_b64":"m5Yr+hiRDVCdDq5kr9XMV6IGIx87gYCpMTzzmA/vXkOGZh7/f0rxh0rSXqJmK1078242fZitcsnKsYzg0vGjCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"109d1eb98a930b4355cf667c453a4d54732fda73c3a03c101a39873e24dfebd2","last_reissued_at":"2026-07-05T11:08:41.097680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:41.097680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.NE"],"primary_cat":"cs.LG","authors_text":"Marzieh Najariyan, Mehran Mazandarani","submitted_at":"2025-05-02T09:08:07Z","abstract_excerpt":"This article introduces Perception-Informed Neural Networks (PrINNs), a framework designed to incorporate perception-based information into neural networks, addressing both systems with known and unknown physics laws or differential equations. Moreover, PrINNs extend the concept of Physics-Informed Neural Networks (PINNs) and their variants, offering a platform for the integration of diverse forms of perception precisiation, including singular, probability distribution, possibility distribution, interval, and fuzzy graph. In fact, PrINNs allow neural networks to model dynamical systems by inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.03806","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/2505.03806/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":"2505.03806","created_at":"2026-07-05T11:08:41.097737+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.03806v2","created_at":"2026-07-05T11:08:41.097737+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.03806","created_at":"2026-07-05T11:08:41.097737+00:00"},{"alias_kind":"pith_short_12","alias_value":"CCOR5OMKSMFU","created_at":"2026-07-05T11:08:41.097737+00:00"},{"alias_kind":"pith_short_16","alias_value":"CCOR5OMKSMFUGVOP","created_at":"2026-07-05T11:08:41.097737+00:00"},{"alias_kind":"pith_short_8","alias_value":"CCOR5OMK","created_at":"2026-07-05T11:08:41.097737+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR","json":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR.json","graph_json":"https://pith.science/api/pith-number/CCOR5OMKSMFUGVOPMZ6EKOSNKR/graph.json","events_json":"https://pith.science/api/pith-number/CCOR5OMKSMFUGVOPMZ6EKOSNKR/events.json","paper":"https://pith.science/paper/CCOR5OMK"},"agent_actions":{"view_html":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR","download_json":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR.json","view_paper":"https://pith.science/paper/CCOR5OMK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.03806&json=true","fetch_graph":"https://pith.science/api/pith-number/CCOR5OMKSMFUGVOPMZ6EKOSNKR/graph.json","fetch_events":"https://pith.science/api/pith-number/CCOR5OMKSMFUGVOPMZ6EKOSNKR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR/action/storage_attestation","attest_author":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR/action/author_attestation","sign_citation":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR/action/citation_signature","submit_replication":"https://pith.science/pith/CCOR5OMKSMFUGVOPMZ6EKOSNKR/action/replication_record"}},"created_at":"2026-07-05T11:08:41.097737+00:00","updated_at":"2026-07-05T11:08:41.097737+00:00"}