{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WGKGUXIHR73GYEIDTCC4LCZWFA","short_pith_number":"pith:WGKGUXIH","canonical_record":{"source":{"id":"2605.29213","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-05-28T00:52:27Z","cross_cats_sorted":["cs.CE","cs.NA","math-ph","math.MP"],"title_canon_sha256":"ae016c441d37ab6953e87e876d86a3fd4b0675d2c5ff0a39db34bb61b6eebd12","abstract_canon_sha256":"13b4241278ff7e3b4af23d23cce52892b81d9a98aac3e8809f64eedf1aac0242"},"schema_version":"1.0"},"canonical_sha256":"b1946a5d078ff66c11039885c58b36283c8f97854d82d1c1188b977a884d4016","source":{"kind":"arxiv","id":"2605.29213","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.29213","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.29213v1","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29213","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"WGKGUXIHR73G","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"pith_short_16","alias_value":"WGKGUXIHR73GYEID","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"pith_short_8","alias_value":"WGKGUXIH","created_at":"2026-05-29T01:05:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WGKGUXIHR73GYEIDTCC4LCZWFA","target":"record","payload":{"canonical_record":{"source":{"id":"2605.29213","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-05-28T00:52:27Z","cross_cats_sorted":["cs.CE","cs.NA","math-ph","math.MP"],"title_canon_sha256":"ae016c441d37ab6953e87e876d86a3fd4b0675d2c5ff0a39db34bb61b6eebd12","abstract_canon_sha256":"13b4241278ff7e3b4af23d23cce52892b81d9a98aac3e8809f64eedf1aac0242"},"schema_version":"1.0"},"canonical_sha256":"b1946a5d078ff66c11039885c58b36283c8f97854d82d1c1188b977a884d4016","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:24.629829Z","signature_b64":"FHZt94/K/CQeEeP9LT+3zIC2n1HbrvOoDvGINWopbVwk76P+79y23J91GLSrFWBLzCVyqS+HI9pLqaUUejZdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1946a5d078ff66c11039885c58b36283c8f97854d82d1c1188b977a884d4016","last_reissued_at":"2026-05-29T01:05:24.629185Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:24.629185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.29213","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":"2026-05-29T01:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"N3Yq6WhAuW479qRL6L4mUN/CjxS9vHPUg6ol8hvX9yVsSg4IyYklCvMfrZnyX7AQU6WlK0HwH87+xWNurYo3Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:15:20.588935Z"},"content_sha256":"f940552e04d5ee716d4a3ba73147c43300f4873d61031cb49e6f625e118df4eb","schema_version":"1.0","event_id":"sha256:f940552e04d5ee716d4a3ba73147c43300f4873d61031cb49e6f625e118df4eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WGKGUXIHR73GYEIDTCC4LCZWFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multifidelity Proper Orthogonal Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CE","cs.NA","math-ph","math.MP"],"primary_cat":"math.NA","authors_text":"Karen Willcox, Nicole Aretz","submitted_at":"2026-05-28T00:52:27Z","abstract_excerpt":"This paper introduces a multifidelity formulation that reduces the computational cost of the proper orthogonal decomposition (POD) of a high-fidelity model by leveraging data from cheaper, lower-fidelity models. POD is a prevalent technique for extracting a low-dimensional basis from training data to achieve subsequent dimension reduction or reduced-order modeling. In scientific and engineering applications, the training data are typically numerical snapshot solutions of a high-fidelity model, and computation of a sufficiently rich snapshot set can be prohibitively expensive, especially when s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29213","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/2605.29213/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":"2026-05-29T01:05:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kvAixEY8df5EAs1lpGmwmQk2+RrkWVOyv3xXZgKYc1mxxC3eqFDIvWdbtmDSOcnpZfCaxPnznENxHxSalsF+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T20:15:20.589637Z"},"content_sha256":"3ff700707af3ffde621fe8f2fbd18e5127d5194eccb47540d5d750fab1abc479","schema_version":"1.0","event_id":"sha256:3ff700707af3ffde621fe8f2fbd18e5127d5194eccb47540d5d750fab1abc479"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WGKGUXIHR73GYEIDTCC4LCZWFA/bundle.json","state_url":"https://pith.science/pith/WGKGUXIHR73GYEIDTCC4LCZWFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WGKGUXIHR73GYEIDTCC4LCZWFA/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":"2026-06-08T20:15:20Z","links":{"resolver":"https://pith.science/pith/WGKGUXIHR73GYEIDTCC4LCZWFA","bundle":"https://pith.science/pith/WGKGUXIHR73GYEIDTCC4LCZWFA/bundle.json","state":"https://pith.science/pith/WGKGUXIHR73GYEIDTCC4LCZWFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WGKGUXIHR73GYEIDTCC4LCZWFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WGKGUXIHR73GYEIDTCC4LCZWFA","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":"13b4241278ff7e3b4af23d23cce52892b81d9a98aac3e8809f64eedf1aac0242","cross_cats_sorted":["cs.CE","cs.NA","math-ph","math.MP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-05-28T00:52:27Z","title_canon_sha256":"ae016c441d37ab6953e87e876d86a3fd4b0675d2c5ff0a39db34bb61b6eebd12"},"schema_version":"1.0","source":{"id":"2605.29213","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.29213","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"arxiv_version","alias_value":"2605.29213v1","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29213","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"pith_short_12","alias_value":"WGKGUXIHR73G","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"pith_short_16","alias_value":"WGKGUXIHR73GYEID","created_at":"2026-05-29T01:05:24Z"},{"alias_kind":"pith_short_8","alias_value":"WGKGUXIH","created_at":"2026-05-29T01:05:24Z"}],"graph_snapshots":[{"event_id":"sha256:3ff700707af3ffde621fe8f2fbd18e5127d5194eccb47540d5d750fab1abc479","target":"graph","created_at":"2026-05-29T01:05:24Z","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/2605.29213/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper introduces a multifidelity formulation that reduces the computational cost of the proper orthogonal decomposition (POD) of a high-fidelity model by leveraging data from cheaper, lower-fidelity models. POD is a prevalent technique for extracting a low-dimensional basis from training data to achieve subsequent dimension reduction or reduced-order modeling. In scientific and engineering applications, the training data are typically numerical snapshot solutions of a high-fidelity model, and computation of a sufficiently rich snapshot set can be prohibitively expensive, especially when s","authors_text":"Karen Willcox, Nicole Aretz","cross_cats":["cs.CE","cs.NA","math-ph","math.MP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-05-28T00:52:27Z","title":"Multifidelity Proper Orthogonal Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29213","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:f940552e04d5ee716d4a3ba73147c43300f4873d61031cb49e6f625e118df4eb","target":"record","created_at":"2026-05-29T01:05:24Z","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":"13b4241278ff7e3b4af23d23cce52892b81d9a98aac3e8809f64eedf1aac0242","cross_cats_sorted":["cs.CE","cs.NA","math-ph","math.MP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2026-05-28T00:52:27Z","title_canon_sha256":"ae016c441d37ab6953e87e876d86a3fd4b0675d2c5ff0a39db34bb61b6eebd12"},"schema_version":"1.0","source":{"id":"2605.29213","kind":"arxiv","version":1}},"canonical_sha256":"b1946a5d078ff66c11039885c58b36283c8f97854d82d1c1188b977a884d4016","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b1946a5d078ff66c11039885c58b36283c8f97854d82d1c1188b977a884d4016","first_computed_at":"2026-05-29T01:05:24.629185Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T01:05:24.629185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FHZt94/K/CQeEeP9LT+3zIC2n1HbrvOoDvGINWopbVwk76P+79y23J91GLSrFWBLzCVyqS+HI9pLqaUUejZdAg==","signature_status":"signed_v1","signed_at":"2026-05-29T01:05:24.629829Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.29213","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f940552e04d5ee716d4a3ba73147c43300f4873d61031cb49e6f625e118df4eb","sha256:3ff700707af3ffde621fe8f2fbd18e5127d5194eccb47540d5d750fab1abc479"],"state_sha256":"4fffd46c1cb117d12d8423ae8f6139aef37ed289b5060b61bf5e9ee70b77789b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fK4saqnr6vi4qH5IluSycBu4VR15xKusdNM3KpKmtDP0gt5VxtxT+SCxJq83WRq9bmmlq0h9nZ3o7R6yUQ0uBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T20:15:20.593190Z","bundle_sha256":"269a9d8de97e94d82de35b2f8aab5d02144174d04604b29abc9806fdbe8a7d6f"}}