{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DYQGSXRNQGNWVLHJAF763PPDGL","short_pith_number":"pith:DYQGSXRN","canonical_record":{"source":{"id":"2505.04263","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-07T09:13:00Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"aec6324b94e34faca6ac032ce2dd5fb9669ce2ae751b20edd81cad7ba0248e7c","abstract_canon_sha256":"bc2e5ce95652ba9e88b0038855a252d09fd495c81f7e4dd90cdba08c32d58746"},"schema_version":"1.0"},"canonical_sha256":"1e20695e2d819b6aace9017fedbde332efbc1e0e0e340954b388cfe8e929ebbb","source":{"kind":"arxiv","id":"2505.04263","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.04263","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"arxiv_version","alias_value":"2505.04263v1","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04263","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"pith_short_12","alias_value":"DYQGSXRNQGNW","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"pith_short_16","alias_value":"DYQGSXRNQGNWVLHJ","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"pith_short_8","alias_value":"DYQGSXRN","created_at":"2026-07-05T10:59:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DYQGSXRNQGNWVLHJAF763PPDGL","target":"record","payload":{"canonical_record":{"source":{"id":"2505.04263","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-07T09:13:00Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"aec6324b94e34faca6ac032ce2dd5fb9669ce2ae751b20edd81cad7ba0248e7c","abstract_canon_sha256":"bc2e5ce95652ba9e88b0038855a252d09fd495c81f7e4dd90cdba08c32d58746"},"schema_version":"1.0"},"canonical_sha256":"1e20695e2d819b6aace9017fedbde332efbc1e0e0e340954b388cfe8e929ebbb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:38.966628Z","signature_b64":"dC+m41iv8pEzhG4J36BwPTf+qvR3lJ9P2UdJdXAvGF0glqUXsjNyoluF0CsGfOLKuMJyZlyV/CR34uNYYpOlDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e20695e2d819b6aace9017fedbde332efbc1e0e0e340954b388cfe8e929ebbb","last_reissued_at":"2026-07-05T10:59:38.965500Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:38.965500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.04263","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":"2026-07-05T10:59:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tclr5HyY/DkwIpGSKodRRWdKKQ6lCmw473qVFqjqzy/WhhyqadS2iYf4mq+CSbCKPhfcYtcwRC1PMi0dXKCEAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T11:02:04.305853Z"},"content_sha256":"0242f7e82acaa31032061a9e88694164931e9a44fa5305dae54c1167aad8f1c2","schema_version":"1.0","event_id":"sha256:0242f7e82acaa31032061a9e88694164931e9a44fa5305dae54c1167aad8f1c2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DYQGSXRNQGNWVLHJAF763PPDGL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA","math.NA"],"primary_cat":"cs.LG","authors_text":"Jan Blechschmidt, Jan-F. Pietschmann, Martin Stoll, Max Winkler, Tom-Christian Riemer","submitted_at":"2025-05-07T09:13:00Z","abstract_excerpt":"We develop a novel physics informed deep learning approach for solving nonlinear drift-diffusion equations on metric graphs. These models represent an important model class with a large number of applications in areas ranging from transport in biological cells to the motion of human crowds. While traditional numerical schemes require a large amount of tailoring, especially in the case of model design or parameter identification problems, physics informed deep operator networks (DeepONet) have emerged as a versatile tool for the solution of partial differential equations with the particular adv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04263","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/2505.04263/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":"2026-07-05T10:59:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wVdeHqa1h2tKPeaPCjlBamfj9O2721zt0trRhQNWRWvUESPgkbUS4fNj2VR8hhsUnM1shjmPXbhJwO3B4vdYCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T11:02:04.306375Z"},"content_sha256":"24caea578680492422aa81fbb8e32a5bd7f1926e843e3a38ce8b8b75ee59e4c0","schema_version":"1.0","event_id":"sha256:24caea578680492422aa81fbb8e32a5bd7f1926e843e3a38ce8b8b75ee59e4c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DYQGSXRNQGNWVLHJAF763PPDGL/bundle.json","state_url":"https://pith.science/pith/DYQGSXRNQGNWVLHJAF763PPDGL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DYQGSXRNQGNWVLHJAF763PPDGL/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":"2026-08-21T11:02:04Z","links":{"resolver":"https://pith.science/pith/DYQGSXRNQGNWVLHJAF763PPDGL","bundle":"https://pith.science/pith/DYQGSXRNQGNWVLHJAF763PPDGL/bundle.json","state":"https://pith.science/pith/DYQGSXRNQGNWVLHJAF763PPDGL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DYQGSXRNQGNWVLHJAF763PPDGL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DYQGSXRNQGNWVLHJAF763PPDGL","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":"bc2e5ce95652ba9e88b0038855a252d09fd495c81f7e4dd90cdba08c32d58746","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-07T09:13:00Z","title_canon_sha256":"aec6324b94e34faca6ac032ce2dd5fb9669ce2ae751b20edd81cad7ba0248e7c"},"schema_version":"1.0","source":{"id":"2505.04263","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.04263","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"arxiv_version","alias_value":"2505.04263v1","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.04263","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"pith_short_12","alias_value":"DYQGSXRNQGNW","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"pith_short_16","alias_value":"DYQGSXRNQGNWVLHJ","created_at":"2026-07-05T10:59:38Z"},{"alias_kind":"pith_short_8","alias_value":"DYQGSXRN","created_at":"2026-07-05T10:59:38Z"}],"graph_snapshots":[{"event_id":"sha256:24caea578680492422aa81fbb8e32a5bd7f1926e843e3a38ce8b8b75ee59e4c0","target":"graph","created_at":"2026-07-05T10:59:38Z","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/2505.04263/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We develop a novel physics informed deep learning approach for solving nonlinear drift-diffusion equations on metric graphs. These models represent an important model class with a large number of applications in areas ranging from transport in biological cells to the motion of human crowds. While traditional numerical schemes require a large amount of tailoring, especially in the case of model design or parameter identification problems, physics informed deep operator networks (DeepONet) have emerged as a versatile tool for the solution of partial differential equations with the particular adv","authors_text":"Jan Blechschmidt, Jan-F. Pietschmann, Martin Stoll, Max Winkler, Tom-Christian Riemer","cross_cats":["cs.NA","math.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-07T09:13:00Z","title":"Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.04263","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":"sha256:0242f7e82acaa31032061a9e88694164931e9a44fa5305dae54c1167aad8f1c2","target":"record","created_at":"2026-07-05T10:59:38Z","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":"bc2e5ce95652ba9e88b0038855a252d09fd495c81f7e4dd90cdba08c32d58746","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-07T09:13:00Z","title_canon_sha256":"aec6324b94e34faca6ac032ce2dd5fb9669ce2ae751b20edd81cad7ba0248e7c"},"schema_version":"1.0","source":{"id":"2505.04263","kind":"arxiv","version":1}},"canonical_sha256":"1e20695e2d819b6aace9017fedbde332efbc1e0e0e340954b388cfe8e929ebbb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1e20695e2d819b6aace9017fedbde332efbc1e0e0e340954b388cfe8e929ebbb","first_computed_at":"2026-07-05T10:59:38.965500Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:59:38.965500Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dC+m41iv8pEzhG4J36BwPTf+qvR3lJ9P2UdJdXAvGF0glqUXsjNyoluF0CsGfOLKuMJyZlyV/CR34uNYYpOlDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:59:38.966628Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.04263","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0242f7e82acaa31032061a9e88694164931e9a44fa5305dae54c1167aad8f1c2","sha256:24caea578680492422aa81fbb8e32a5bd7f1926e843e3a38ce8b8b75ee59e4c0"],"state_sha256":"7068a226ba9dd8d4b3a19fe012fc104d82fd7bb579ff568f9453801e929d4ba5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nXz487pCrF+yDVBLHq/d2HENH+KKTiVQ5OezAzgMvoAKLKBYQqHeE8MNyJZg+oGpsnNQFa5MHi4/xOQM4/cpCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T11:02:04.311244Z","bundle_sha256":"6d86642b3787da753f1b088bceacfa18efa62c45242ad54b477be3dc76513033"}}