{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:MTW5R3JMI7XQ2HNGY3QLSDVOO4","short_pith_number":"pith:MTW5R3JM","canonical_record":{"source":{"id":"2502.14432","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T10:33:21Z","cross_cats_sorted":[],"title_canon_sha256":"5f665c3be38efa2e745788df0cfec59aeb87184e3a00c1c816ca9076b0a325b3","abstract_canon_sha256":"583deb8e369714fde41c6d0dba26a12d94752a14e1f53d07f3db9c99cd470d53"},"schema_version":"1.0"},"canonical_sha256":"64edd8ed2c47ef0d1da6c6e0b90eae7724ff6745460b1b99608deae9266543a4","source":{"kind":"arxiv","id":"2502.14432","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14432","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14432v1","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14432","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"pith_short_12","alias_value":"MTW5R3JMI7XQ","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"pith_short_16","alias_value":"MTW5R3JMI7XQ2HNG","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"pith_short_8","alias_value":"MTW5R3JM","created_at":"2026-07-05T10:17:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:MTW5R3JMI7XQ2HNGY3QLSDVOO4","target":"record","payload":{"canonical_record":{"source":{"id":"2502.14432","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T10:33:21Z","cross_cats_sorted":[],"title_canon_sha256":"5f665c3be38efa2e745788df0cfec59aeb87184e3a00c1c816ca9076b0a325b3","abstract_canon_sha256":"583deb8e369714fde41c6d0dba26a12d94752a14e1f53d07f3db9c99cd470d53"},"schema_version":"1.0"},"canonical_sha256":"64edd8ed2c47ef0d1da6c6e0b90eae7724ff6745460b1b99608deae9266543a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:26.362910Z","signature_b64":"7a+pljVaSmpgeyO0SQyBlGVt7H+N6rzup24A9RMcmcrhNRdBcsE+40nVgxkFiFtCpO9WXHGRFFnPmBKHHT+EDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64edd8ed2c47ef0d1da6c6e0b90eae7724ff6745460b1b99608deae9266543a4","last_reissued_at":"2026-07-05T10:17:26.362412Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:26.362412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.14432","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:17:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UjVxHvMWfbGafCov4ACKi9zgLVs7OmOvnTMgrZdE3vLGpIjmmxDWXcisJf0j1T9dyGFDS2BX8/2ZTr4J/oihBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:13:59.449080Z"},"content_sha256":"27435df263d9ae4434854f61b147bb82867d3f4cec31be7d17659ebdf49c347d","schema_version":"1.0","event_id":"sha256:27435df263d9ae4434854f61b147bb82867d3f4cec31be7d17659ebdf49c347d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:MTW5R3JMI7XQ2HNGY3QLSDVOO4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Port-Hamiltonian Neural Networks with Output Error Noise Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gerben I. Beintema, Maarten Schoukens, Nick Jaensson, Roland T\\'oth, Sarvin Moradi","submitted_at":"2025-02-20T10:33:21Z","abstract_excerpt":"Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural networks. However, their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements. This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors. We integrate port-Hamiltonian theory into the neural network structure, allowing for the inclusion of external inputs an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14432","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/2502.14432/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:17:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kuTNxLTiYtmWW9ZYU37YC/BIgNS99JvNyf952YwXta7k9lve9imtJwu5BZy68tWnnkKZR4Sf5b3yUvE2AiAICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T17:13:59.450003Z"},"content_sha256":"80472c5907aae76b9905b3bd93811e489e828e2c9403f93e1891681018fa27e6","schema_version":"1.0","event_id":"sha256:80472c5907aae76b9905b3bd93811e489e828e2c9403f93e1891681018fa27e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4/bundle.json","state_url":"https://pith.science/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-03T17:13:59Z","links":{"resolver":"https://pith.science/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4","bundle":"https://pith.science/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4/bundle.json","state":"https://pith.science/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MTW5R3JMI7XQ2HNGY3QLSDVOO4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MTW5R3JMI7XQ2HNGY3QLSDVOO4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"583deb8e369714fde41c6d0dba26a12d94752a14e1f53d07f3db9c99cd470d53","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T10:33:21Z","title_canon_sha256":"5f665c3be38efa2e745788df0cfec59aeb87184e3a00c1c816ca9076b0a325b3"},"schema_version":"1.0","source":{"id":"2502.14432","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14432","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14432v1","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14432","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"pith_short_12","alias_value":"MTW5R3JMI7XQ","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"pith_short_16","alias_value":"MTW5R3JMI7XQ2HNG","created_at":"2026-07-05T10:17:26Z"},{"alias_kind":"pith_short_8","alias_value":"MTW5R3JM","created_at":"2026-07-05T10:17:26Z"}],"graph_snapshots":[{"event_id":"sha256:80472c5907aae76b9905b3bd93811e489e828e2c9403f93e1891681018fa27e6","target":"graph","created_at":"2026-07-05T10:17:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.14432/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural networks. However, their direct application to engineering systems is often challenged by practical issues, including the presence of external inputs, dissipation, and noisy measurements. This paper introduces a novel framework that enhances the capabilities of HNNs to address these real-life factors. We integrate port-Hamiltonian theory into the neural network structure, allowing for the inclusion of external inputs an","authors_text":"Gerben I. Beintema, Maarten Schoukens, Nick Jaensson, Roland T\\'oth, Sarvin Moradi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T10:33:21Z","title":"Port-Hamiltonian Neural Networks with Output Error Noise Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14432","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:27435df263d9ae4434854f61b147bb82867d3f4cec31be7d17659ebdf49c347d","target":"record","created_at":"2026-07-05T10:17:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"583deb8e369714fde41c6d0dba26a12d94752a14e1f53d07f3db9c99cd470d53","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-20T10:33:21Z","title_canon_sha256":"5f665c3be38efa2e745788df0cfec59aeb87184e3a00c1c816ca9076b0a325b3"},"schema_version":"1.0","source":{"id":"2502.14432","kind":"arxiv","version":1}},"canonical_sha256":"64edd8ed2c47ef0d1da6c6e0b90eae7724ff6745460b1b99608deae9266543a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64edd8ed2c47ef0d1da6c6e0b90eae7724ff6745460b1b99608deae9266543a4","first_computed_at":"2026-07-05T10:17:26.362412Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:17:26.362412Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7a+pljVaSmpgeyO0SQyBlGVt7H+N6rzup24A9RMcmcrhNRdBcsE+40nVgxkFiFtCpO9WXHGRFFnPmBKHHT+EDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:17:26.362910Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.14432","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:27435df263d9ae4434854f61b147bb82867d3f4cec31be7d17659ebdf49c347d","sha256:80472c5907aae76b9905b3bd93811e489e828e2c9403f93e1891681018fa27e6"],"state_sha256":"7fd1ad60eaa0ecd7945f046c0b56dc70760cd1e596cc5aceb847e9071c27c4f9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7wIe4MeDn4cQs9W/1NthopyLhJfQAYQ916e+2sQSJQLYsrjWsFNwSDshlCL/jnUUQWQ3UHP1rqD9I9gvpI2vDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T17:13:59.455979Z","bundle_sha256":"8be9707b83acf85800cd8e8630cffc6a021470bc0dad445b2d3b626281a50ad8"}}