{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2AVPKVMUMTS3L4664GVGJHTQXV","short_pith_number":"pith:2AVPKVMU","schema_version":"1.0","canonical_sha256":"d02af5559464e5b5f3dee1aa649e70bd4a1fa2368dfca8ba6109ad168a831e5a","source":{"kind":"arxiv","id":"2503.17400","version":2},"attestation_state":"computed","paper":{"title":"TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Angela Dai, Faez Ahmed, Mohamed Elrefaie, Qian Chen","submitted_at":"2025-03-19T17:30:57Z","abstract_excerpt":"Surrogate modeling has emerged as a powerful tool to accelerate Computational Fluid Dynamics (CFD) simulations. Existing 3D geometric learning models based on point clouds, voxels, meshes, or graphs depend on explicit geometric representations that are memory-intensive and resolution-limited. For large-scale simulations with millions of nodes and cells, existing models require aggressive downsampling due to their dependence on mesh resolution, resulting in degraded accuracy. We present TripNet, a triplane-based neural framework that implicitly encodes 3D geometry into a compact, continuous fea"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2503.17400","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2025-03-19T17:30:57Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ca248644d51d753172cbc5b61a5c18593c22b597b68488183a37c48c7f38437e","abstract_canon_sha256":"84ac707fe66702bd29717ce7ff7c79f72583d2d957173ed4b67d39bffb80f06d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:02.751533Z","signature_b64":"2FtqDGwQzW4BPHnr0XP6J1sI8XqbS9nI23LGYRNa7+zlUyFP3zVn/ngimmieDtS42ekf8ysQZUmUmshwizTpDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d02af5559464e5b5f3dee1aa649e70bd4a1fa2368dfca8ba6109ad168a831e5a","last_reissued_at":"2026-06-09T02:07:02.750420Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:02.750420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Angela Dai, Faez Ahmed, Mohamed Elrefaie, Qian Chen","submitted_at":"2025-03-19T17:30:57Z","abstract_excerpt":"Surrogate modeling has emerged as a powerful tool to accelerate Computational Fluid Dynamics (CFD) simulations. Existing 3D geometric learning models based on point clouds, voxels, meshes, or graphs depend on explicit geometric representations that are memory-intensive and resolution-limited. For large-scale simulations with millions of nodes and cells, existing models require aggressive downsampling due to their dependence on mesh resolution, resulting in degraded accuracy. We present TripNet, a triplane-based neural framework that implicitly encodes 3D geometry into a compact, continuous fea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.17400","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/2503.17400/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.17400","created_at":"2026-06-09T02:07:02.750582+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.17400v2","created_at":"2026-06-09T02:07:02.750582+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.17400","created_at":"2026-06-09T02:07:02.750582+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AVPKVMUMTS3","created_at":"2026-06-09T02:07:02.750582+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AVPKVMUMTS3L466","created_at":"2026-06-09T02:07:02.750582+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AVPKVMU","created_at":"2026-06-09T02:07:02.750582+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":6,"sample":[{"citing_arxiv_id":"2512.03280","citing_title":"BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft","ref_index":33,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07098","citing_title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","ref_index":55,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04474","citing_title":"Geometry-Aware Neural Optimizer for Shape Optimization and Inversion","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04474","citing_title":"Geometry-Aware Neural Optimizer for Shape Optimization and Inversion","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04474","citing_title":"Geometry-Aware Neural Optimizer for Shape Optimization and Inversion","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07098","citing_title":"CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation","ref_index":73,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV","json":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV.json","graph_json":"https://pith.science/api/pith-number/2AVPKVMUMTS3L4664GVGJHTQXV/graph.json","events_json":"https://pith.science/api/pith-number/2AVPKVMUMTS3L4664GVGJHTQXV/events.json","paper":"https://pith.science/paper/2AVPKVMU"},"agent_actions":{"view_html":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV","download_json":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV.json","view_paper":"https://pith.science/paper/2AVPKVMU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.17400&json=true","fetch_graph":"https://pith.science/api/pith-number/2AVPKVMUMTS3L4664GVGJHTQXV/graph.json","fetch_events":"https://pith.science/api/pith-number/2AVPKVMUMTS3L4664GVGJHTQXV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV/action/storage_attestation","attest_author":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV/action/author_attestation","sign_citation":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV/action/citation_signature","submit_replication":"https://pith.science/pith/2AVPKVMUMTS3L4664GVGJHTQXV/action/replication_record"}},"created_at":"2026-06-09T02:07:02.750582+00:00","updated_at":"2026-06-09T02:07:02.750582+00:00"}