{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2BQAZOFGYJYVVXTPNHTOHYNQB2","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":"41d09be2e18de3f5c232b63d752957d0a0503f1c4b3cb7579e69fd2d7c70b921","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-06-13T07:21:49Z","title_canon_sha256":"ba841a9f3f88a3a4dff08f9a64df7daa522c3a22bd335133d1a602a31bbf8159"},"schema_version":"1.0","source":{"id":"2506.11518","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11518","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11518v2","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11518","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"2BQAZOFGYJYV","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"2BQAZOFGYJYVVXTP","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"2BQAZOFG","created_at":"2026-06-03T01:05:44Z"}],"graph_snapshots":[{"event_id":"sha256:4110a44c818c918e11eabe81f005f0f07700e8cd0a20b74d95f9f0e860bbe229","target":"graph","created_at":"2026-06-03T01:05:44Z","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/2506.11518/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose transformed Diffsuion-Wave fractional Physics-Informed Neural Networks (tDWfPINNs) for efficiently solving time-fractional diffusion-wave equations with fractional order $\\alpha\\in(1,2)$. Conventional numerical methods for these equations often compromise the mesh-free advantage of Physics-Informed Neural Networks (PINNs) or impose high computational costs when computing fractional derivatives. The proposed method avoids first-order derivative calculations at quadrature points by introducing an integrand transformation technique, significantly reducing computational costs associated","authors_text":"Jing Li, Zhengqi Zhang","cross_cats":["cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-06-13T07:21:49Z","title":"Transformed Diffusion-Wave fPINNs: Enhancing Computing Efficiency for PINNs Solving Time-Fractional Diffusion-Wave Equations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11518","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:e2253ad430ac13dfb760db284453629b8f2c7b357b650ef69336892dfd4459fe","target":"record","created_at":"2026-06-03T01:05:44Z","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":"41d09be2e18de3f5c232b63d752957d0a0503f1c4b3cb7579e69fd2d7c70b921","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-06-13T07:21:49Z","title_canon_sha256":"ba841a9f3f88a3a4dff08f9a64df7daa522c3a22bd335133d1a602a31bbf8159"},"schema_version":"1.0","source":{"id":"2506.11518","kind":"arxiv","version":2}},"canonical_sha256":"d0600cb8a6c2715ade6f69e6e3e1b00e93c1c94cc6ed757018435f33269f2e4f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d0600cb8a6c2715ade6f69e6e3e1b00e93c1c94cc6ed757018435f33269f2e4f","first_computed_at":"2026-06-03T01:05:44.231364Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:44.231364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X1Wba15tipvdeBjML++fxA7HuzCdMJ4dcWU2/Bn5SwwBsmlh8yv3FxGn7swtPBhHxHheRn4y7ncuGycgsF33DQ==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:44.231826Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.11518","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2253ad430ac13dfb760db284453629b8f2c7b357b650ef69336892dfd4459fe","sha256:4110a44c818c918e11eabe81f005f0f07700e8cd0a20b74d95f9f0e860bbe229"],"state_sha256":"b969aa1ea7ca4ba435e898fc8006f93514536654a8467244a92794db56c2ec91"}