{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FVGGJ2FB2WVYEXYO46AKDYCSFN","short_pith_number":"pith:FVGGJ2FB","schema_version":"1.0","canonical_sha256":"2d4c64e8a1d5ab825f0ee780a1e0522b6b1cd0ae06b7d5a29a544fd98425839d","source":{"kind":"arxiv","id":"2601.03673","version":2},"attestation_state":"computed","paper":{"title":"Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ibai Ramirez, Joel Pino, Jokin Alcibar, Jose I. Aizpurua, Mikel Sanz","submitted_at":"2026-01-07T07:54:09Z","abstract_excerpt":"Physics-Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities. Most existing PINN-based prognostics approaches are deterministic or account only for epistemic uncertainty, limiting their suitability for risk-aware decision-making. This work introduces a heteroscedastic Bayesian Physics-Informed Neural Network (B-PINN) framework that jointly models epistemic and aleatoric uncertainty, yielding full predictiv"},"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":"2601.03673","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-07T07:54:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d99efedaaab68fe4c711577149138a58a7601cfee8ec555bd30183eb654118c4","abstract_canon_sha256":"3c31ac96241bd490fb2b088a9c948ad74133f9bd5baa59b97276e45b73284c08"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-24T01:14:24.537987Z","signature_b64":"lNz3vtGerCuKfJ7YJJmhnNqY8lGLSvOIggEO49zygL+nK7fig0MuEpDZH6sktpmLlidYC5U9TB6sH3amrOfcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d4c64e8a1d5ab825f0ee780a1e0522b6b1cd0ae06b7d5a29a544fd98425839d","last_reissued_at":"2026-06-24T01:14:24.537523Z","signature_status":"signed_v1","first_computed_at":"2026-06-24T01:14:24.537523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ibai Ramirez, Joel Pino, Jokin Alcibar, Jose I. Aizpurua, Mikel Sanz","submitted_at":"2026-01-07T07:54:09Z","abstract_excerpt":"Physics-Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities. Most existing PINN-based prognostics approaches are deterministic or account only for epistemic uncertainty, limiting their suitability for risk-aware decision-making. This work introduces a heteroscedastic Bayesian Physics-Informed Neural Network (B-PINN) framework that jointly models epistemic and aleatoric uncertainty, yielding full predictiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.03673","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/2601.03673/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":"2601.03673","created_at":"2026-06-24T01:14:24.537582+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.03673v2","created_at":"2026-06-24T01:14:24.537582+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.03673","created_at":"2026-06-24T01:14:24.537582+00:00"},{"alias_kind":"pith_short_12","alias_value":"FVGGJ2FB2WVY","created_at":"2026-06-24T01:14:24.537582+00:00"},{"alias_kind":"pith_short_16","alias_value":"FVGGJ2FB2WVYEXYO","created_at":"2026-06-24T01:14:24.537582+00:00"},{"alias_kind":"pith_short_8","alias_value":"FVGGJ2FB","created_at":"2026-06-24T01:14:24.537582+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/FVGGJ2FB2WVYEXYO46AKDYCSFN","json":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN.json","graph_json":"https://pith.science/api/pith-number/FVGGJ2FB2WVYEXYO46AKDYCSFN/graph.json","events_json":"https://pith.science/api/pith-number/FVGGJ2FB2WVYEXYO46AKDYCSFN/events.json","paper":"https://pith.science/paper/FVGGJ2FB"},"agent_actions":{"view_html":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN","download_json":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN.json","view_paper":"https://pith.science/paper/FVGGJ2FB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.03673&json=true","fetch_graph":"https://pith.science/api/pith-number/FVGGJ2FB2WVYEXYO46AKDYCSFN/graph.json","fetch_events":"https://pith.science/api/pith-number/FVGGJ2FB2WVYEXYO46AKDYCSFN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN/action/storage_attestation","attest_author":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN/action/author_attestation","sign_citation":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN/action/citation_signature","submit_replication":"https://pith.science/pith/FVGGJ2FB2WVYEXYO46AKDYCSFN/action/replication_record"}},"created_at":"2026-06-24T01:14:24.537582+00:00","updated_at":"2026-06-24T01:14:24.537582+00:00"}