{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RPNGTHGQWR7R7RAZRNUZAI4G3B","short_pith_number":"pith:RPNGTHGQ","canonical_record":{"source":{"id":"2205.01897","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-05-04T05:19:46Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"4666891041de8fa338ac250d18a8c36056edbed718b4a354647b67cb6a463904","abstract_canon_sha256":"6e980af679353a0ec59ca698cd3b4af9e2144e8110ee496dd6b605baa2853cd0"},"schema_version":"1.0"},"canonical_sha256":"8bda699cd0b47f1fc4198b69902386d87f0c0dd0fe514dfc3d795a99e7502324","source":{"kind":"arxiv","id":"2205.01897","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01897","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01897v3","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01897","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"pith_short_12","alias_value":"RPNGTHGQWR7R","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"pith_short_16","alias_value":"RPNGTHGQWR7R7RAZ","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"pith_short_8","alias_value":"RPNGTHGQ","created_at":"2026-07-05T04:46:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RPNGTHGQWR7R7RAZRNUZAI4G3B","target":"record","payload":{"canonical_record":{"source":{"id":"2205.01897","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-05-04T05:19:46Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"4666891041de8fa338ac250d18a8c36056edbed718b4a354647b67cb6a463904","abstract_canon_sha256":"6e980af679353a0ec59ca698cd3b4af9e2144e8110ee496dd6b605baa2853cd0"},"schema_version":"1.0"},"canonical_sha256":"8bda699cd0b47f1fc4198b69902386d87f0c0dd0fe514dfc3d795a99e7502324","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:46:37.385334Z","signature_b64":"MuNH/EbwaNXPmRC8VrgkY0jSLmIbFtREVHPbXW6idBjG1PzJ0khy0t3A2pvItLA/uZGAAgoo6MJg6YC2skYdDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bda699cd0b47f1fc4198b69902386d87f0c0dd0fe514dfc3d795a99e7502324","last_reissued_at":"2026-07-05T04:46:37.384793Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:46:37.384793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.01897","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:46:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hV2bb6uH1Mko9AC9u0tdUoli2+DfBiRj5VwMrPHsbribDEPgvc3Gm1xQwFwc1eFwT8zHDcAma5CvFdBtTLJeBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T20:16:06.931474Z"},"content_sha256":"5d1aece4ef5929b27eabf86a910dd5ec674fc492a9d44e7bfed7cc0cb7ead356","schema_version":"1.0","event_id":"sha256:5d1aece4ef5929b27eabf86a910dd5ec674fc492a9d44e7bfed7cc0cb7ead356"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RPNGTHGQWR7R7RAZRNUZAI4G3B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Alec Wright, Emanu\\\"el Habets, Jan Wilczek, Vesa V\\\"alim\\\"aki","submitted_at":"2022-05-04T05:19:46Z","abstract_excerpt":"Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable to state-of-the-art recurrent neural networks (RNNs) albeit using fewer parameters. We show that this approach does not require oversampling and allows to increase the sampling rate after the training has completed, which results in increas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01897","kind":"arxiv","version":3},"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/2205.01897/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":"2026-07-05T04:46:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MvT++fMlAbKdkb4xXHewEiZEull/dM2WD/bhvRHkSpcXWyRQwyHpavIDT7fiaKUG6LU3464hYqoo76q+BuMlAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T20:16:06.931996Z"},"content_sha256":"3be3aaa68fc6c1d9186b2876e2e94b0cc47c34c6fcc7291b32518f0f7a9bfbe1","schema_version":"1.0","event_id":"sha256:3be3aaa68fc6c1d9186b2876e2e94b0cc47c34c6fcc7291b32518f0f7a9bfbe1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B/bundle.json","state_url":"https://pith.science/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B/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":"2026-07-31T20:16:06Z","links":{"resolver":"https://pith.science/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B","bundle":"https://pith.science/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B/bundle.json","state":"https://pith.science/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RPNGTHGQWR7R7RAZRNUZAI4G3B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RPNGTHGQWR7R7RAZRNUZAI4G3B","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":"6e980af679353a0ec59ca698cd3b4af9e2144e8110ee496dd6b605baa2853cd0","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-05-04T05:19:46Z","title_canon_sha256":"4666891041de8fa338ac250d18a8c36056edbed718b4a354647b67cb6a463904"},"schema_version":"1.0","source":{"id":"2205.01897","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01897","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01897v3","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01897","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"pith_short_12","alias_value":"RPNGTHGQWR7R","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"pith_short_16","alias_value":"RPNGTHGQWR7R7RAZ","created_at":"2026-07-05T04:46:37Z"},{"alias_kind":"pith_short_8","alias_value":"RPNGTHGQ","created_at":"2026-07-05T04:46:37Z"}],"graph_snapshots":[{"event_id":"sha256:3be3aaa68fc6c1d9186b2876e2e94b0cc47c34c6fcc7291b32518f0f7a9bfbe1","target":"graph","created_at":"2026-07-05T04:46:37Z","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/2205.01897/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable to state-of-the-art recurrent neural networks (RNNs) albeit using fewer parameters. We show that this approach does not require oversampling and allows to increase the sampling rate after the training has completed, which results in increas","authors_text":"Alec Wright, Emanu\\\"el Habets, Jan Wilczek, Vesa V\\\"alim\\\"aki","cross_cats":["cs.LG","cs.SD"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-05-04T05:19:46Z","title":"Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01897","kind":"arxiv","version":3},"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":"sha256:5d1aece4ef5929b27eabf86a910dd5ec674fc492a9d44e7bfed7cc0cb7ead356","target":"record","created_at":"2026-07-05T04:46:37Z","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":"6e980af679353a0ec59ca698cd3b4af9e2144e8110ee496dd6b605baa2853cd0","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-05-04T05:19:46Z","title_canon_sha256":"4666891041de8fa338ac250d18a8c36056edbed718b4a354647b67cb6a463904"},"schema_version":"1.0","source":{"id":"2205.01897","kind":"arxiv","version":3}},"canonical_sha256":"8bda699cd0b47f1fc4198b69902386d87f0c0dd0fe514dfc3d795a99e7502324","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8bda699cd0b47f1fc4198b69902386d87f0c0dd0fe514dfc3d795a99e7502324","first_computed_at":"2026-07-05T04:46:37.384793Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:46:37.384793Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MuNH/EbwaNXPmRC8VrgkY0jSLmIbFtREVHPbXW6idBjG1PzJ0khy0t3A2pvItLA/uZGAAgoo6MJg6YC2skYdDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:46:37.385334Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.01897","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5d1aece4ef5929b27eabf86a910dd5ec674fc492a9d44e7bfed7cc0cb7ead356","sha256:3be3aaa68fc6c1d9186b2876e2e94b0cc47c34c6fcc7291b32518f0f7a9bfbe1"],"state_sha256":"87911f32a631431dfb35ea28981e3fe22ed7ff99ae5d2161652d4c7f7ba07540"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9jXQ0GdkHcuO3UPD3MXptiG+2RnrAycVwIiA/jcC7f0z3jfknimZ7rkx/gmISZPVMr9coQWjKKElbUVltLmgBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T20:16:06.938598Z","bundle_sha256":"9f42248cbafffbd83baa95a05da9902616ed686b15018c9562c80a45c5b13aaa"}}