{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AX2DM4M3UYZQO5XEDGJVD4LHXR","short_pith_number":"pith:AX2DM4M3","schema_version":"1.0","canonical_sha256":"05f436719ba6330776e4199351f167bc61fa307d91dc42b6d50fe7fb95940d5d","source":{"kind":"arxiv","id":"2606.03679","version":1},"attestation_state":"computed","paper":{"title":"From Well-Posed Inversion to Learning Design: Physics- Informed Neural Estimation for Autonomic Regulation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Arnaud Boutin, Francois Cottin, Giuseppe Alessio D'Inverno, Sara Nour Sadoun, Taous-Meriem Laleg-Kirati","submitted_at":"2026-06-02T14:01:34Z","abstract_excerpt":"Learning-based and physics-informed methods are increasingly used for inverse estimation in controlled nonlinear dynamical systems. However, in many such approaches, the theoretic requirements that make unknown-input reconstruction meaningful, namely well-posedness in the sense of Hadamard, are often disregarded or weakly addressed through generic regularization terms with no explicit guarantees. In this work, we adopt a complementary viewpoint in which these control-theoretic and structural conditions inform the estimator design and constrain its training. We thus develop a physics-informed i"},"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":"2606.03679","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SY","submitted_at":"2026-06-02T14:01:34Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"8619a0c20065c6f302baddadb391d6968b663f545de0a1507906cd23a3131111","abstract_canon_sha256":"4ac9fe75e6d24b28768c55c67c2fddb352b4230c5de6aa30be5ea1262968c4e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:06:04.363827Z","signature_b64":"CIG1gCgUMFOxCRJvuf04vCYagn9BOadwMBBCOnJFiCeQbRo6NG2WazRYTOSKNF2JAEg+9xqdpZa3dTYR5n8SCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05f436719ba6330776e4199351f167bc61fa307d91dc42b6d50fe7fb95940d5d","last_reissued_at":"2026-06-03T01:06:04.363423Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:06:04.363423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Well-Posed Inversion to Learning Design: Physics- Informed Neural Estimation for Autonomic Regulation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Arnaud Boutin, Francois Cottin, Giuseppe Alessio D'Inverno, Sara Nour Sadoun, Taous-Meriem Laleg-Kirati","submitted_at":"2026-06-02T14:01:34Z","abstract_excerpt":"Learning-based and physics-informed methods are increasingly used for inverse estimation in controlled nonlinear dynamical systems. However, in many such approaches, the theoretic requirements that make unknown-input reconstruction meaningful, namely well-posedness in the sense of Hadamard, are often disregarded or weakly addressed through generic regularization terms with no explicit guarantees. In this work, we adopt a complementary viewpoint in which these control-theoretic and structural conditions inform the estimator design and constrain its training. We thus develop a physics-informed i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.03679","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/2606.03679/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":"2606.03679","created_at":"2026-06-03T01:06:04.363481+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.03679v1","created_at":"2026-06-03T01:06:04.363481+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.03679","created_at":"2026-06-03T01:06:04.363481+00:00"},{"alias_kind":"pith_short_12","alias_value":"AX2DM4M3UYZQ","created_at":"2026-06-03T01:06:04.363481+00:00"},{"alias_kind":"pith_short_16","alias_value":"AX2DM4M3UYZQO5XE","created_at":"2026-06-03T01:06:04.363481+00:00"},{"alias_kind":"pith_short_8","alias_value":"AX2DM4M3","created_at":"2026-06-03T01:06:04.363481+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/AX2DM4M3UYZQO5XEDGJVD4LHXR","json":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR.json","graph_json":"https://pith.science/api/pith-number/AX2DM4M3UYZQO5XEDGJVD4LHXR/graph.json","events_json":"https://pith.science/api/pith-number/AX2DM4M3UYZQO5XEDGJVD4LHXR/events.json","paper":"https://pith.science/paper/AX2DM4M3"},"agent_actions":{"view_html":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR","download_json":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR.json","view_paper":"https://pith.science/paper/AX2DM4M3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.03679&json=true","fetch_graph":"https://pith.science/api/pith-number/AX2DM4M3UYZQO5XEDGJVD4LHXR/graph.json","fetch_events":"https://pith.science/api/pith-number/AX2DM4M3UYZQO5XEDGJVD4LHXR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR/action/storage_attestation","attest_author":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR/action/author_attestation","sign_citation":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR/action/citation_signature","submit_replication":"https://pith.science/pith/AX2DM4M3UYZQO5XEDGJVD4LHXR/action/replication_record"}},"created_at":"2026-06-03T01:06:04.363481+00:00","updated_at":"2026-06-03T01:06:04.363481+00:00"}