{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:X452YMHJN4HJ7GJ7VAIXEPLNGJ","short_pith_number":"pith:X452YMHJ","canonical_record":{"source":{"id":"2502.05044","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T16:09:25Z","cross_cats_sorted":[],"title_canon_sha256":"1c7b814f0ea8f96e6862ae6ccf72d31979a651caf795a7f86a35840aeedbbf64","abstract_canon_sha256":"7816a5c7fbfcfef94058a489d56507f25763b10c82031f75879328591f8fd392"},"schema_version":"1.0"},"canonical_sha256":"bf3bac30e96f0e9f993fa811723d6d32459d86be400efddddd23b090027b932e","source":{"kind":"arxiv","id":"2502.05044","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05044","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05044v2","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05044","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"pith_short_12","alias_value":"X452YMHJN4HJ","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"pith_short_16","alias_value":"X452YMHJN4HJ7GJ7","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"pith_short_8","alias_value":"X452YMHJ","created_at":"2026-07-05T11:35:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:X452YMHJN4HJ7GJ7VAIXEPLNGJ","target":"record","payload":{"canonical_record":{"source":{"id":"2502.05044","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T16:09:25Z","cross_cats_sorted":[],"title_canon_sha256":"1c7b814f0ea8f96e6862ae6ccf72d31979a651caf795a7f86a35840aeedbbf64","abstract_canon_sha256":"7816a5c7fbfcfef94058a489d56507f25763b10c82031f75879328591f8fd392"},"schema_version":"1.0"},"canonical_sha256":"bf3bac30e96f0e9f993fa811723d6d32459d86be400efddddd23b090027b932e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:12.018386Z","signature_b64":"1dDUVGmBZzOZUw4wDarChhHQ0+OYRY6FI5UAAsY1zCpWjgYmh55LfrpT+qlmAVS7YtdKP9s/k+o74o3+JosxAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf3bac30e96f0e9f993fa811723d6d32459d86be400efddddd23b090027b932e","last_reissued_at":"2026-07-05T11:35:12.017856Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:12.017856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.05044","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":"2026-07-05T11:35:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4UG/z2nqkTD8lOl/cAYpjwm1KwrefDeNRNzrLc0yxfDiwE2dOxAcm8BiHWqq6d6n+srbboGzExsuZgQFaxGkCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T02:14:10.851365Z"},"content_sha256":"422d9a00c95517031482cd5688622c5c7d86ce116f69c930643632eb187f3dd5","schema_version":"1.0","event_id":"sha256:422d9a00c95517031482cd5688622c5c7d86ce116f69c930643632eb187f3dd5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:X452YMHJN4HJ7GJ7VAIXEPLNGJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David May, Denis Korolev, Dinesh K. Natarajan, Michael Hinterm\\\"uller, Miro Duhovic, Stefano Cassola, Tim Schmidt","submitted_at":"2025-02-07T16:09:25Z","abstract_excerpt":"This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach evaluates the efficiency and accuracy of different scale-bridging methodologies combining traditional surrogate models and even integrating physics-informed neural networks (PINNs) with numerical solvers, enabling accurate permeability predictions across micro- and mesoscales. Four methodologies were evaluated: Single Scale Method (SSM), Simple Upscaling Met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05044","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/2502.05044/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":"2026-07-05T11:35:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Yq48Rfc3EDxtMC2fwewfW+lhFyplyj3E6YH6+C6AIKcgHX/VW1v+oU/CYG2FWNws6KtqlxJbL6ZP/mTzyaQiDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T02:14:10.852196Z"},"content_sha256":"618a42968989ebdbea6b8ae134fd94c7af1febe997a0ff256b22a80ce063dc8b","schema_version":"1.0","event_id":"sha256:618a42968989ebdbea6b8ae134fd94c7af1febe997a0ff256b22a80ce063dc8b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ/bundle.json","state_url":"https://pith.science/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ/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":"2026-08-11T02:14:10Z","links":{"resolver":"https://pith.science/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ","bundle":"https://pith.science/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ/bundle.json","state":"https://pith.science/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X452YMHJN4HJ7GJ7VAIXEPLNGJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:X452YMHJN4HJ7GJ7VAIXEPLNGJ","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":"7816a5c7fbfcfef94058a489d56507f25763b10c82031f75879328591f8fd392","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T16:09:25Z","title_canon_sha256":"1c7b814f0ea8f96e6862ae6ccf72d31979a651caf795a7f86a35840aeedbbf64"},"schema_version":"1.0","source":{"id":"2502.05044","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05044","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05044v2","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05044","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"pith_short_12","alias_value":"X452YMHJN4HJ","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"pith_short_16","alias_value":"X452YMHJN4HJ7GJ7","created_at":"2026-07-05T11:35:12Z"},{"alias_kind":"pith_short_8","alias_value":"X452YMHJ","created_at":"2026-07-05T11:35:12Z"}],"graph_snapshots":[{"event_id":"sha256:618a42968989ebdbea6b8ae134fd94c7af1febe997a0ff256b22a80ce063dc8b","target":"graph","created_at":"2026-07-05T11:35:12Z","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/2502.05044/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures. By addressing the computational challenges inherent to multiscale modeling, the proposed approach evaluates the efficiency and accuracy of different scale-bridging methodologies combining traditional surrogate models and even integrating physics-informed neural networks (PINNs) with numerical solvers, enabling accurate permeability predictions across micro- and mesoscales. Four methodologies were evaluated: Single Scale Method (SSM), Simple Upscaling Met","authors_text":"David May, Denis Korolev, Dinesh K. Natarajan, Michael Hinterm\\\"uller, Miro Duhovic, Stefano Cassola, Tim Schmidt","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T16:09:25Z","title":"Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05044","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":"sha256:422d9a00c95517031482cd5688622c5c7d86ce116f69c930643632eb187f3dd5","target":"record","created_at":"2026-07-05T11:35:12Z","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":"7816a5c7fbfcfef94058a489d56507f25763b10c82031f75879328591f8fd392","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T16:09:25Z","title_canon_sha256":"1c7b814f0ea8f96e6862ae6ccf72d31979a651caf795a7f86a35840aeedbbf64"},"schema_version":"1.0","source":{"id":"2502.05044","kind":"arxiv","version":2}},"canonical_sha256":"bf3bac30e96f0e9f993fa811723d6d32459d86be400efddddd23b090027b932e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bf3bac30e96f0e9f993fa811723d6d32459d86be400efddddd23b090027b932e","first_computed_at":"2026-07-05T11:35:12.017856Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:35:12.017856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1dDUVGmBZzOZUw4wDarChhHQ0+OYRY6FI5UAAsY1zCpWjgYmh55LfrpT+qlmAVS7YtdKP9s/k+o74o3+JosxAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:35:12.018386Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.05044","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:422d9a00c95517031482cd5688622c5c7d86ce116f69c930643632eb187f3dd5","sha256:618a42968989ebdbea6b8ae134fd94c7af1febe997a0ff256b22a80ce063dc8b"],"state_sha256":"22629f673c9c2b00d0f4b79470b2882329168352b65b23a8dde32296bb32a0bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AsqgnuCw62Oe30JEKiHpIxQwqpM3nllfstm0bzRgeP302Zi167IJmktwWyyzIF1Cwb70MjkeAQzM8rqwQG+jDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T02:14:10.859526Z","bundle_sha256":"2b631ea84c491cd8d20fe287fced25bd6cca7d857e7d8bba3bddc0e96266c829"}}