{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OYLNV3PM5FDDDTPHF3FAJJH66W","short_pith_number":"pith:OYLNV3PM","schema_version":"1.0","canonical_sha256":"7616daedece94631cde72eca04a4fef5a1d1aef875e6e68a8e48b7babc048cb2","source":{"kind":"arxiv","id":"2403.17785","version":1},"attestation_state":"computed","paper":{"title":"Neural Distributed Controllers with Port-Hamiltonian Structures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Giancarlo Ferrari-Trecate, Muhammad Zakwan","submitted_at":"2024-03-26T15:17:55Z","abstract_excerpt":"Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly simple scenarios. Parameterizing these policies using Neural Networks (NNs) can deliver good performance, but their sensitivity to small input changes can destabilize the closed-loop system. This paper addresses this issue for a network of nonlinear dissipative systems. Specifically, we leverage well-established port-Hamiltonian structures to"},"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":"2403.17785","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-03-26T15:17:55Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"f27af902fd0f8eb788c900f9ae707e19d4b7c46ee20725d32b160c44089564fe","abstract_canon_sha256":"515786dc6833cd435b380232b0b406aead20c4c8c3a968d53919bd373375b216"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:00:55.314624Z","signature_b64":"pkK5M3SN1KIV39AFegFdAA5QAzuAhO4W1T4Az2HB8rUPl1ChBNgtnK1Fp3wSXbN0XSr1LX/xtiFX6Xh6saShBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7616daedece94631cde72eca04a4fef5a1d1aef875e6e68a8e48b7babc048cb2","last_reissued_at":"2026-07-05T08:00:55.314145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:00:55.314145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Distributed Controllers with Port-Hamiltonian Structures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Giancarlo Ferrari-Trecate, Muhammad Zakwan","submitted_at":"2024-03-26T15:17:55Z","abstract_excerpt":"Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly simple scenarios. Parameterizing these policies using Neural Networks (NNs) can deliver good performance, but their sensitivity to small input changes can destabilize the closed-loop system. This paper addresses this issue for a network of nonlinear dissipative systems. Specifically, we leverage well-established port-Hamiltonian structures to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.17785","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/2403.17785/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":"2403.17785","created_at":"2026-07-05T08:00:55.314201+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.17785v1","created_at":"2026-07-05T08:00:55.314201+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.17785","created_at":"2026-07-05T08:00:55.314201+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYLNV3PM5FDD","created_at":"2026-07-05T08:00:55.314201+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYLNV3PM5FDDDTPH","created_at":"2026-07-05T08:00:55.314201+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYLNV3PM","created_at":"2026-07-05T08:00:55.314201+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.10096","citing_title":"Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach","ref_index":39,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W","json":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W.json","graph_json":"https://pith.science/api/pith-number/OYLNV3PM5FDDDTPHF3FAJJH66W/graph.json","events_json":"https://pith.science/api/pith-number/OYLNV3PM5FDDDTPHF3FAJJH66W/events.json","paper":"https://pith.science/paper/OYLNV3PM"},"agent_actions":{"view_html":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W","download_json":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W.json","view_paper":"https://pith.science/paper/OYLNV3PM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.17785&json=true","fetch_graph":"https://pith.science/api/pith-number/OYLNV3PM5FDDDTPHF3FAJJH66W/graph.json","fetch_events":"https://pith.science/api/pith-number/OYLNV3PM5FDDDTPHF3FAJJH66W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W/action/storage_attestation","attest_author":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W/action/author_attestation","sign_citation":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W/action/citation_signature","submit_replication":"https://pith.science/pith/OYLNV3PM5FDDDTPHF3FAJJH66W/action/replication_record"}},"created_at":"2026-07-05T08:00:55.314201+00:00","updated_at":"2026-07-05T08:00:55.314201+00:00"}