{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OFS4YJQO3UEDZA6QRLVN3S2JXU","short_pith_number":"pith:OFS4YJQO","canonical_record":{"source":{"id":"2308.13819","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-26T09:00:31Z","cross_cats_sorted":["cs.NA","math.DS","math.NA"],"title_canon_sha256":"f12933543b02350cabece8d037fb293a5ce71902d2cd18590a6648d6934b289b","abstract_canon_sha256":"8d06e8d28e13af76c4a8c66f54929a932bd7e43cb0ad58535b1e3f6804c6392e"},"schema_version":"1.0"},"canonical_sha256":"7165cc260edd083c83d08aeaddcb49bd0dc6385a3e3e98829f686fb6a22dd4de","source":{"kind":"arxiv","id":"2308.13819","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.13819","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"arxiv_version","alias_value":"2308.13819v2","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13819","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"pith_short_12","alias_value":"OFS4YJQO3UED","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"pith_short_16","alias_value":"OFS4YJQO3UEDZA6Q","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"pith_short_8","alias_value":"OFS4YJQO","created_at":"2026-07-05T07:30:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OFS4YJQO3UEDZA6QRLVN3S2JXU","target":"record","payload":{"canonical_record":{"source":{"id":"2308.13819","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-26T09:00:31Z","cross_cats_sorted":["cs.NA","math.DS","math.NA"],"title_canon_sha256":"f12933543b02350cabece8d037fb293a5ce71902d2cd18590a6648d6934b289b","abstract_canon_sha256":"8d06e8d28e13af76c4a8c66f54929a932bd7e43cb0ad58535b1e3f6804c6392e"},"schema_version":"1.0"},"canonical_sha256":"7165cc260edd083c83d08aeaddcb49bd0dc6385a3e3e98829f686fb6a22dd4de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:30:52.613946Z","signature_b64":"vH39atu/4xdb0kvHPvcYdpNmMf6INaDbJpNEcCTk864jFXhsfSEATh8QdQ8ucNFSGLqYpcm4fZ//aI87wxUJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7165cc260edd083c83d08aeaddcb49bd0dc6385a3e3e98829f686fb6a22dd4de","last_reissued_at":"2026-07-05T07:30:52.613449Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:30:52.613449Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.13819","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:30:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"78Gu3e4ZkE9+4n8UgQQbOKYB60vkQxak0Izg/pJOSpW/Qxn1hsn56f+VQ/iGvUluXEx/WCksDB0fZ4sK+esKCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:01:34.992238Z"},"content_sha256":"7ed049f96517f1a960a0e2b20fa1c1438322e12920941c1fb6de91dac97c2c3d","schema_version":"1.0","event_id":"sha256:7ed049f96517f1a960a0e2b20fa1c1438322e12920941c1fb6de91dac97c2c3d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OFS4YJQO3UEDZA6QRLVN3S2JXU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA","math.DS","math.NA"],"primary_cat":"cs.LG","authors_text":"Igor Pontes Duff, Pawan Goyal, Peter Benner","submitted_at":"2023-08-26T09:00:31Z","abstract_excerpt":"Scientific machine learning for inferring dynamical systems combines data-driven modeling, physics-based modeling, and empirical knowledge. It plays an essential role in engineering design and digital twinning. In this work, we primarily focus on an operator inference methodology that builds dynamical models, preferably in low-dimension, with a prior hypothesis on the model structure, often determined by known physics or given by experts. Then, for inference, we aim to learn the operators of a model by setting up an appropriate optimization problem. One of the critical properties of dynamical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13819","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/2308.13819/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":"2026-07-05T07:30:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ACphOr+WFYFryTssunAE9ZJbMTNv4VOb5MHhUIqJVprLXP0WuuQJifZ4w9v81/omQA35nM8KfU96spS4AJdCDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T12:01:34.992970Z"},"content_sha256":"9e39790c22fda4857b0d525eb67770683e8d605fca143b7ec2a56b9f285d3b1d","schema_version":"1.0","event_id":"sha256:9e39790c22fda4857b0d525eb67770683e8d605fca143b7ec2a56b9f285d3b1d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU/bundle.json","state_url":"https://pith.science/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU/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":"2026-08-12T12:01:34Z","links":{"resolver":"https://pith.science/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU","bundle":"https://pith.science/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU/bundle.json","state":"https://pith.science/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OFS4YJQO3UEDZA6QRLVN3S2JXU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OFS4YJQO3UEDZA6QRLVN3S2JXU","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":"8d06e8d28e13af76c4a8c66f54929a932bd7e43cb0ad58535b1e3f6804c6392e","cross_cats_sorted":["cs.NA","math.DS","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-26T09:00:31Z","title_canon_sha256":"f12933543b02350cabece8d037fb293a5ce71902d2cd18590a6648d6934b289b"},"schema_version":"1.0","source":{"id":"2308.13819","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.13819","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"arxiv_version","alias_value":"2308.13819v2","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13819","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"pith_short_12","alias_value":"OFS4YJQO3UED","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"pith_short_16","alias_value":"OFS4YJQO3UEDZA6Q","created_at":"2026-07-05T07:30:52Z"},{"alias_kind":"pith_short_8","alias_value":"OFS4YJQO","created_at":"2026-07-05T07:30:52Z"}],"graph_snapshots":[{"event_id":"sha256:9e39790c22fda4857b0d525eb67770683e8d605fca143b7ec2a56b9f285d3b1d","target":"graph","created_at":"2026-07-05T07:30:52Z","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/2308.13819/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scientific machine learning for inferring dynamical systems combines data-driven modeling, physics-based modeling, and empirical knowledge. It plays an essential role in engineering design and digital twinning. In this work, we primarily focus on an operator inference methodology that builds dynamical models, preferably in low-dimension, with a prior hypothesis on the model structure, often determined by known physics or given by experts. Then, for inference, we aim to learn the operators of a model by setting up an appropriate optimization problem. One of the critical properties of dynamical ","authors_text":"Igor Pontes Duff, Pawan Goyal, Peter Benner","cross_cats":["cs.NA","math.DS","math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-26T09:00:31Z","title":"Guaranteed Stable Quadratic Models and their applications in SINDy and Operator Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13819","kind":"arxiv","version":2},"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":"sha256:7ed049f96517f1a960a0e2b20fa1c1438322e12920941c1fb6de91dac97c2c3d","target":"record","created_at":"2026-07-05T07:30:52Z","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":"8d06e8d28e13af76c4a8c66f54929a932bd7e43cb0ad58535b1e3f6804c6392e","cross_cats_sorted":["cs.NA","math.DS","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-08-26T09:00:31Z","title_canon_sha256":"f12933543b02350cabece8d037fb293a5ce71902d2cd18590a6648d6934b289b"},"schema_version":"1.0","source":{"id":"2308.13819","kind":"arxiv","version":2}},"canonical_sha256":"7165cc260edd083c83d08aeaddcb49bd0dc6385a3e3e98829f686fb6a22dd4de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7165cc260edd083c83d08aeaddcb49bd0dc6385a3e3e98829f686fb6a22dd4de","first_computed_at":"2026-07-05T07:30:52.613449Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:30:52.613449Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vH39atu/4xdb0kvHPvcYdpNmMf6INaDbJpNEcCTk864jFXhsfSEATh8QdQ8ucNFSGLqYpcm4fZ//aI87wxUJAA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:30:52.613946Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.13819","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7ed049f96517f1a960a0e2b20fa1c1438322e12920941c1fb6de91dac97c2c3d","sha256:9e39790c22fda4857b0d525eb67770683e8d605fca143b7ec2a56b9f285d3b1d"],"state_sha256":"ba5ef96f44da346d494da5d4937608cf96198b4c331c5583c46ef580879b9ab9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t3dDD+Fu2aM9VSFbblaizdhgrX+Q/Q7l6yhMTU8J9h+dxbX5DcrWIfobwJpx6WzQ/pjWQDIQuWlKMtqa+FnxDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T12:01:34.997057Z","bundle_sha256":"eee83623c62f09b481cf2b6cfb980dde54a1429a3e3f94bc59a1e50ef07a1e99"}}