{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:S2ANE6OURBBBPSZMHHV5MNKTLB","short_pith_number":"pith:S2ANE6OU","canonical_record":{"source":{"id":"2501.02762","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-06T05:03:08Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"579455de26dfc4f15bfa1770b413883bda1f80ed61d14dc04668b9ce165c697f","abstract_canon_sha256":"e1f4819aac39f3aad66425e8e479f551cf9732e692e83a484dad81d86443f192"},"schema_version":"1.0"},"canonical_sha256":"9680d279d4884217cb2c39ebd635535864bd769e6435ac804985bee664b94019","source":{"kind":"arxiv","id":"2501.02762","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.02762","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"arxiv_version","alias_value":"2501.02762v1","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.02762","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"pith_short_12","alias_value":"S2ANE6OURBBB","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"pith_short_16","alias_value":"S2ANE6OURBBBPSZM","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"pith_short_8","alias_value":"S2ANE6OU","created_at":"2026-07-05T09:57:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:S2ANE6OURBBBPSZMHHV5MNKTLB","target":"record","payload":{"canonical_record":{"source":{"id":"2501.02762","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-06T05:03:08Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"579455de26dfc4f15bfa1770b413883bda1f80ed61d14dc04668b9ce165c697f","abstract_canon_sha256":"e1f4819aac39f3aad66425e8e479f551cf9732e692e83a484dad81d86443f192"},"schema_version":"1.0"},"canonical_sha256":"9680d279d4884217cb2c39ebd635535864bd769e6435ac804985bee664b94019","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:11.769639Z","signature_b64":"Gzbx3p/xdBZiBEH4RQVAHdfYY3y4W5QAYAVff2Y2g8p5/PUm59Udv4qUE35oYq92f+OGkTPnohX2cS5RJYCcBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9680d279d4884217cb2c39ebd635535864bd769e6435ac804985bee664b94019","last_reissued_at":"2026-07-05T09:57:11.769179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:11.769179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.02762","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":"2026-07-05T09:57:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s3w0wzZyegB4ntNxaDkC2eFajVeFETfKpzqpbaP5BTECTSQ4vpG2M9NvgkGLiSfwVjjgKrLNjqhbaCnIzdDhBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T13:59:41.169757Z"},"content_sha256":"1a797d97970062a11dd27efddecaa175d33aaa9e5e59e66333756ec9a339075c","schema_version":"1.0","event_id":"sha256:1a797d97970062a11dd27efddecaa175d33aaa9e5e59e66333756ec9a339075c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:S2ANE6OURBBBPSZMHHV5MNKTLB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaled-cPIKANs: Domain Scaling in Chebyshev-based Physics-informed Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Farinaz Mostajeran, Salah A Faroughi","submitted_at":"2025-01-06T05:03:08Z","abstract_excerpt":"Partial Differential Equations (PDEs) are integral to modeling many scientific and engineering problems. Physics-informed Neural Networks (PINNs) have emerged as promising tools for solving PDEs by embedding governing equations into the neural network loss function. However, when dealing with PDEs characterized by strong oscillatory dynamics over large computational domains, PINNs based on Multilayer Perceptrons (MLPs) often exhibit poor convergence and reduced accuracy. To address these challenges, this paper introduces Scaled-cPIKAN, a physics-informed architecture rooted in Kolmogorov-Arnol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.02762","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/2501.02762/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":"2026-07-05T09:57:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ts5W2Y9GK9fFN1e7iDSw2mMjpGZRKTQcPvnhdV7Ayur7de1v8T/H+XcmTI1dFaxF2uwa8sD7FHCxLzf2+bkrBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T13:59:41.170242Z"},"content_sha256":"07e5acad3f4cd0c7d9c9312b7fb464cebed566d0eb532b3042ed4ff35f951b82","schema_version":"1.0","event_id":"sha256:07e5acad3f4cd0c7d9c9312b7fb464cebed566d0eb532b3042ed4ff35f951b82"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/S2ANE6OURBBBPSZMHHV5MNKTLB/bundle.json","state_url":"https://pith.science/pith/S2ANE6OURBBBPSZMHHV5MNKTLB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/S2ANE6OURBBBPSZMHHV5MNKTLB/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":"2026-08-04T13:59:41Z","links":{"resolver":"https://pith.science/pith/S2ANE6OURBBBPSZMHHV5MNKTLB","bundle":"https://pith.science/pith/S2ANE6OURBBBPSZMHHV5MNKTLB/bundle.json","state":"https://pith.science/pith/S2ANE6OURBBBPSZMHHV5MNKTLB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/S2ANE6OURBBBPSZMHHV5MNKTLB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:S2ANE6OURBBBPSZMHHV5MNKTLB","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":"e1f4819aac39f3aad66425e8e479f551cf9732e692e83a484dad81d86443f192","cross_cats_sorted":["cs.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-06T05:03:08Z","title_canon_sha256":"579455de26dfc4f15bfa1770b413883bda1f80ed61d14dc04668b9ce165c697f"},"schema_version":"1.0","source":{"id":"2501.02762","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.02762","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"arxiv_version","alias_value":"2501.02762v1","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.02762","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"pith_short_12","alias_value":"S2ANE6OURBBB","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"pith_short_16","alias_value":"S2ANE6OURBBBPSZM","created_at":"2026-07-05T09:57:11Z"},{"alias_kind":"pith_short_8","alias_value":"S2ANE6OU","created_at":"2026-07-05T09:57:11Z"}],"graph_snapshots":[{"event_id":"sha256:07e5acad3f4cd0c7d9c9312b7fb464cebed566d0eb532b3042ed4ff35f951b82","target":"graph","created_at":"2026-07-05T09:57:11Z","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/2501.02762/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Partial Differential Equations (PDEs) are integral to modeling many scientific and engineering problems. Physics-informed Neural Networks (PINNs) have emerged as promising tools for solving PDEs by embedding governing equations into the neural network loss function. However, when dealing with PDEs characterized by strong oscillatory dynamics over large computational domains, PINNs based on Multilayer Perceptrons (MLPs) often exhibit poor convergence and reduced accuracy. To address these challenges, this paper introduces Scaled-cPIKAN, a physics-informed architecture rooted in Kolmogorov-Arnol","authors_text":"Farinaz Mostajeran, Salah A Faroughi","cross_cats":["cs.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-06T05:03:08Z","title":"Scaled-cPIKANs: Domain Scaling in Chebyshev-based Physics-informed Kolmogorov-Arnold Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.02762","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":"sha256:1a797d97970062a11dd27efddecaa175d33aaa9e5e59e66333756ec9a339075c","target":"record","created_at":"2026-07-05T09:57:11Z","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":"e1f4819aac39f3aad66425e8e479f551cf9732e692e83a484dad81d86443f192","cross_cats_sorted":["cs.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-01-06T05:03:08Z","title_canon_sha256":"579455de26dfc4f15bfa1770b413883bda1f80ed61d14dc04668b9ce165c697f"},"schema_version":"1.0","source":{"id":"2501.02762","kind":"arxiv","version":1}},"canonical_sha256":"9680d279d4884217cb2c39ebd635535864bd769e6435ac804985bee664b94019","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9680d279d4884217cb2c39ebd635535864bd769e6435ac804985bee664b94019","first_computed_at":"2026-07-05T09:57:11.769179Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:57:11.769179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Gzbx3p/xdBZiBEH4RQVAHdfYY3y4W5QAYAVff2Y2g8p5/PUm59Udv4qUE35oYq92f+OGkTPnohX2cS5RJYCcBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:57:11.769639Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.02762","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1a797d97970062a11dd27efddecaa175d33aaa9e5e59e66333756ec9a339075c","sha256:07e5acad3f4cd0c7d9c9312b7fb464cebed566d0eb532b3042ed4ff35f951b82"],"state_sha256":"63706fd19af90b4b5e48c4d980ce635cc370375a85b460129b5830ca6a312af6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+5lQ9enO3WwRnQjnoCj4TVVoSrhFea5RGzDoNIGkid2EKRwPDUJ0iU/tnkAIEm2HpOu85OX6GPT9MtTKbbeyBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T13:59:41.216585Z","bundle_sha256":"dcbe513370278ab0a53f1a1d300fe85617035339c518202486f6c097d712ec6f"}}