{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:SGVVUAHDX3SJNZHA6NPU5SOVP4","short_pith_number":"pith:SGVVUAHD","canonical_record":{"source":{"id":"2606.07724","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T16:25:09Z","cross_cats_sorted":[],"title_canon_sha256":"69d2d41699e1ed1e475053795afce6c983bd3fe0d572203f89e8f67a27ac0f45","abstract_canon_sha256":"ece49c87802e5deb2cc36e49a41c381eb5e7aaee84fb7224349381c88ed7f08e"},"schema_version":"1.0"},"canonical_sha256":"91ab5a00e3bee496e4e0f35f4ec9d57f395b1478d12cdcf6bf358ccbcacedacd","source":{"kind":"arxiv","id":"2606.07724","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07724","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07724v1","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07724","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"pith_short_12","alias_value":"SGVVUAHDX3SJ","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"pith_short_16","alias_value":"SGVVUAHDX3SJNZHA","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"pith_short_8","alias_value":"SGVVUAHD","created_at":"2026-06-09T01:04:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:SGVVUAHDX3SJNZHA6NPU5SOVP4","target":"record","payload":{"canonical_record":{"source":{"id":"2606.07724","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T16:25:09Z","cross_cats_sorted":[],"title_canon_sha256":"69d2d41699e1ed1e475053795afce6c983bd3fe0d572203f89e8f67a27ac0f45","abstract_canon_sha256":"ece49c87802e5deb2cc36e49a41c381eb5e7aaee84fb7224349381c88ed7f08e"},"schema_version":"1.0"},"canonical_sha256":"91ab5a00e3bee496e4e0f35f4ec9d57f395b1478d12cdcf6bf358ccbcacedacd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:04:50.405424Z","signature_b64":"mzJAKAoAXc7GM6lkCCLXccshKrp2XAxgkPDZ5G7z/Q+xQiSeU8oTJBa/7ZtudYy/KeATIkcRFB2XCmx1z4h6Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91ab5a00e3bee496e4e0f35f4ec9d57f395b1478d12cdcf6bf358ccbcacedacd","last_reissued_at":"2026-06-09T01:04:50.404947Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:04:50.404947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.07724","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":"2026-06-09T01:04:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6unZBWJ0QOjGPw4uCJ2a4jeJrWEF6SUpcDIB7lGz7UXuOklXe3Tmhg3EAVv6o5Tp0M9F88D/oqEplNtq+TSzAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T15:38:24.898551Z"},"content_sha256":"f3e03cf9398e5ff4b1ff48f4854585a8a8939427e951a29efe9065df73e066f7","schema_version":"1.0","event_id":"sha256:f3e03cf9398e5ff4b1ff48f4854585a8a8939427e951a29efe9065df73e066f7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:SGVVUAHDX3SJNZHA6NPU5SOVP4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Huiyu Yang, Jianchun Wang, Kangkang Qi, Keqi Ding, Rikui Zhang, Yuanwei Bin, Yunpeng Wang, Yuntian Chen","submitted_at":"2026-06-05T16:25:09Z","abstract_excerpt":"High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration. Machine-learning-based surface-field prediction offers a faster alternative if the model can efficiently capture both global flow context and local geometric detail. This work proposes a machine-learning-based method, named the geometry-aware triplane field network (GTF-Net), for vehicle aerodynamic pressure and wall shear stress prediction. GTF-Net constructs triplane features directly from sampled surface points through a shared multilayer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07724","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/2606.07724/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":"2026-06-09T01:04:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rQ1DqShILG4OpQvlQHZtnGmaZmadMtE5uoH89xAsgh8KmmwH4LQE5IjuL/WJ8Av7ReM3iyKRBwGNfGXhnkIRCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T15:38:24.898996Z"},"content_sha256":"88c539ad3efe21e647e970d215ab1542db9537aa274fd20ef4c49ca2b4b4e6c2","schema_version":"1.0","event_id":"sha256:88c539ad3efe21e647e970d215ab1542db9537aa274fd20ef4c49ca2b4b4e6c2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4/bundle.json","state_url":"https://pith.science/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4/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":"2026-06-11T15:38:24Z","links":{"resolver":"https://pith.science/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4","bundle":"https://pith.science/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4/bundle.json","state":"https://pith.science/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SGVVUAHDX3SJNZHA6NPU5SOVP4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SGVVUAHDX3SJNZHA6NPU5SOVP4","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":"ece49c87802e5deb2cc36e49a41c381eb5e7aaee84fb7224349381c88ed7f08e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T16:25:09Z","title_canon_sha256":"69d2d41699e1ed1e475053795afce6c983bd3fe0d572203f89e8f67a27ac0f45"},"schema_version":"1.0","source":{"id":"2606.07724","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07724","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07724v1","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07724","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"pith_short_12","alias_value":"SGVVUAHDX3SJ","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"pith_short_16","alias_value":"SGVVUAHDX3SJNZHA","created_at":"2026-06-09T01:04:50Z"},{"alias_kind":"pith_short_8","alias_value":"SGVVUAHD","created_at":"2026-06-09T01:04:50Z"}],"graph_snapshots":[{"event_id":"sha256:88c539ad3efe21e647e970d215ab1542db9537aa274fd20ef4c49ca2b4b4e6c2","target":"graph","created_at":"2026-06-09T01:04:50Z","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/2606.07724/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-fidelity computational fluid dynamics (CFD) is crucial to vehicle aerodynamic analysis, but its cost still constrains early-stage design exploration. Machine-learning-based surface-field prediction offers a faster alternative if the model can efficiently capture both global flow context and local geometric detail. This work proposes a machine-learning-based method, named the geometry-aware triplane field network (GTF-Net), for vehicle aerodynamic pressure and wall shear stress prediction. GTF-Net constructs triplane features directly from sampled surface points through a shared multilayer","authors_text":"Huiyu Yang, Jianchun Wang, Kangkang Qi, Keqi Ding, Rikui Zhang, Yuanwei Bin, Yunpeng Wang, Yuntian Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T16:25:09Z","title":"A Geometry-Aware Triplane Field Network for Vehicle Aerodynamic Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07724","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:f3e03cf9398e5ff4b1ff48f4854585a8a8939427e951a29efe9065df73e066f7","target":"record","created_at":"2026-06-09T01:04:50Z","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":"ece49c87802e5deb2cc36e49a41c381eb5e7aaee84fb7224349381c88ed7f08e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T16:25:09Z","title_canon_sha256":"69d2d41699e1ed1e475053795afce6c983bd3fe0d572203f89e8f67a27ac0f45"},"schema_version":"1.0","source":{"id":"2606.07724","kind":"arxiv","version":1}},"canonical_sha256":"91ab5a00e3bee496e4e0f35f4ec9d57f395b1478d12cdcf6bf358ccbcacedacd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91ab5a00e3bee496e4e0f35f4ec9d57f395b1478d12cdcf6bf358ccbcacedacd","first_computed_at":"2026-06-09T01:04:50.404947Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:04:50.404947Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mzJAKAoAXc7GM6lkCCLXccshKrp2XAxgkPDZ5G7z/Q+xQiSeU8oTJBa/7ZtudYy/KeATIkcRFB2XCmx1z4h6Dw==","signature_status":"signed_v1","signed_at":"2026-06-09T01:04:50.405424Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07724","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3e03cf9398e5ff4b1ff48f4854585a8a8939427e951a29efe9065df73e066f7","sha256:88c539ad3efe21e647e970d215ab1542db9537aa274fd20ef4c49ca2b4b4e6c2"],"state_sha256":"8a0fc5202bdfec97b3f7d7fd56972cd2cf5df87a447dbdd6b5cfcb2270cd784f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5LRAkAkbTes1rocCxzKSQOtwEWYm/Fi+FXjCd0Xp8LWcecN/tVHJLvNsAoxOKplkC4bAMdYzmq7+ZdIWsox9BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T15:38:24.901243Z","bundle_sha256":"24c1d856f99835e35616b4546ed35c3c9012992f2f6b0012ddb1ec9157982bda"}}