{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:P4IF2TJFMWI52ZZINSJXXEYFIJ","short_pith_number":"pith:P4IF2TJF","canonical_record":{"source":{"id":"2606.23402","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-22T14:26:06Z","cross_cats_sorted":[],"title_canon_sha256":"3fbdd566dd06988a82b21622591ce78033a03181749f285de6bed799779e52d8","abstract_canon_sha256":"4fba8160fe4d867084c742ad961944a42a23fc34f52abfef5749593c7bff86fe"},"schema_version":"1.0"},"canonical_sha256":"7f105d4d256591dd67286c937b93054279449983530e965c61c501a2d3f30dec","source":{"kind":"arxiv","id":"2606.23402","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.23402","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"arxiv_version","alias_value":"2606.23402v1","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23402","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"pith_short_12","alias_value":"P4IF2TJFMWI5","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"pith_short_16","alias_value":"P4IF2TJFMWI52ZZI","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"pith_short_8","alias_value":"P4IF2TJF","created_at":"2026-06-23T03:14:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:P4IF2TJFMWI52ZZINSJXXEYFIJ","target":"record","payload":{"canonical_record":{"source":{"id":"2606.23402","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-22T14:26:06Z","cross_cats_sorted":[],"title_canon_sha256":"3fbdd566dd06988a82b21622591ce78033a03181749f285de6bed799779e52d8","abstract_canon_sha256":"4fba8160fe4d867084c742ad961944a42a23fc34f52abfef5749593c7bff86fe"},"schema_version":"1.0"},"canonical_sha256":"7f105d4d256591dd67286c937b93054279449983530e965c61c501a2d3f30dec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:14:19.109424Z","signature_b64":"srO9Alin9rp8UEHqpEmBzA0GLnhFknrXGG1N4yF/b/AgodIMlYQc2i5VEHaUHYWf3ZAmBKt9y9Zw/wyblnSgCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f105d4d256591dd67286c937b93054279449983530e965c61c501a2d3f30dec","last_reissued_at":"2026-06-23T03:14:19.109009Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:14:19.109009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.23402","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":"2026-06-23T03:14:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xYNWH6kQ1ASUjioVbNifnxDdwxBmdAkO5/WM5/xb0mmvH3+sHBimAJTfJSk+2HtQEuyKJba7vMEegH3ncER4Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:18:54.199804Z"},"content_sha256":"4dc6b4429642bc535a0c088ade51b7c868a43ab2128363c4cd67811a4f27bc54","schema_version":"1.0","event_id":"sha256:4dc6b4429642bc535a0c088ade51b7c868a43ab2128363c4cd67811a4f27bc54"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:P4IF2TJFMWI52ZZINSJXXEYFIJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Physics-Informed Modeling for Wood Thermal Analysis and Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alex John Buckthal, Dim P. Papadopoulos, Isak Worre Foged, Jingren Xie, Ryan Anthony O'Connor","submitted_at":"2026-06-22T14:26:06Z","abstract_excerpt":"Wood materials exhibit complex, spatially varying thermal properties that challenge traditional architectural assumptions of material homogeneity. Although data-driven approaches can directly map wood RGB images to their corresponding thermal responses, they operate as uninterpretable black boxes that prioritize statistical correlation and may absorb experimental noise rather than thermodynamic plausibility. To address these limitations, we present physics-informed deep learning frameworks that integrate partial differential equations (PDEs) to predict pixel-level thermal responses of spatiall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23402","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/2606.23402/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":"2026-06-23T03:14:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rdw55CQs8DwyZsVSxiSzDawrcA8N2DwuDrMwFICftqp6SnFz6Y3QHMNzAadxrnFCs93cHHybcvm0AkdA/SD1Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:18:54.200638Z"},"content_sha256":"62674f32165a8c4aa5df42880f6a5264f828dfb9308773a4daa013c54aaaa1df","schema_version":"1.0","event_id":"sha256:62674f32165a8c4aa5df42880f6a5264f828dfb9308773a4daa013c54aaaa1df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ/bundle.json","state_url":"https://pith.science/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ/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":"2026-08-04T15:18:54Z","links":{"resolver":"https://pith.science/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ","bundle":"https://pith.science/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ/bundle.json","state":"https://pith.science/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P4IF2TJFMWI52ZZINSJXXEYFIJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:P4IF2TJFMWI52ZZINSJXXEYFIJ","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":"4fba8160fe4d867084c742ad961944a42a23fc34f52abfef5749593c7bff86fe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-22T14:26:06Z","title_canon_sha256":"3fbdd566dd06988a82b21622591ce78033a03181749f285de6bed799779e52d8"},"schema_version":"1.0","source":{"id":"2606.23402","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.23402","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"arxiv_version","alias_value":"2606.23402v1","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23402","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"pith_short_12","alias_value":"P4IF2TJFMWI5","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"pith_short_16","alias_value":"P4IF2TJFMWI52ZZI","created_at":"2026-06-23T03:14:19Z"},{"alias_kind":"pith_short_8","alias_value":"P4IF2TJF","created_at":"2026-06-23T03:14:19Z"}],"graph_snapshots":[{"event_id":"sha256:62674f32165a8c4aa5df42880f6a5264f828dfb9308773a4daa013c54aaaa1df","target":"graph","created_at":"2026-06-23T03:14:19Z","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/2606.23402/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Wood materials exhibit complex, spatially varying thermal properties that challenge traditional architectural assumptions of material homogeneity. Although data-driven approaches can directly map wood RGB images to their corresponding thermal responses, they operate as uninterpretable black boxes that prioritize statistical correlation and may absorb experimental noise rather than thermodynamic plausibility. To address these limitations, we present physics-informed deep learning frameworks that integrate partial differential equations (PDEs) to predict pixel-level thermal responses of spatiall","authors_text":"Alex John Buckthal, Dim P. Papadopoulos, Isak Worre Foged, Jingren Xie, Ryan Anthony O'Connor","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-22T14:26:06Z","title":"Physics-Informed Modeling for Wood Thermal Analysis and Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23402","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":"sha256:4dc6b4429642bc535a0c088ade51b7c868a43ab2128363c4cd67811a4f27bc54","target":"record","created_at":"2026-06-23T03:14:19Z","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":"4fba8160fe4d867084c742ad961944a42a23fc34f52abfef5749593c7bff86fe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-22T14:26:06Z","title_canon_sha256":"3fbdd566dd06988a82b21622591ce78033a03181749f285de6bed799779e52d8"},"schema_version":"1.0","source":{"id":"2606.23402","kind":"arxiv","version":1}},"canonical_sha256":"7f105d4d256591dd67286c937b93054279449983530e965c61c501a2d3f30dec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f105d4d256591dd67286c937b93054279449983530e965c61c501a2d3f30dec","first_computed_at":"2026-06-23T03:14:19.109009Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T03:14:19.109009Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"srO9Alin9rp8UEHqpEmBzA0GLnhFknrXGG1N4yF/b/AgodIMlYQc2i5VEHaUHYWf3ZAmBKt9y9Zw/wyblnSgCg==","signature_status":"signed_v1","signed_at":"2026-06-23T03:14:19.109424Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.23402","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4dc6b4429642bc535a0c088ade51b7c868a43ab2128363c4cd67811a4f27bc54","sha256:62674f32165a8c4aa5df42880f6a5264f828dfb9308773a4daa013c54aaaa1df"],"state_sha256":"99bebf19bd633d1e895f0b6f791263ad45c911513db93d143b3e199a22229b6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yUfliqTwf+OhFJg+xNmpjlntf4FBAmZAkJDYvpsi06sQY6aK60SWrLg9McFq7mlcaQHP64kdLejpyWkGmZYUCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:18:54.207715Z","bundle_sha256":"72249ad4cb3a1ed826ca735db6cb5dba5247503748593b8f307c3e9630f7ffb7"}}