{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:47CGZQSIVTF4ZT2SVESMVHZYQ7","short_pith_number":"pith:47CGZQSI","schema_version":"1.0","canonical_sha256":"e7c46cc248accbcccf52a924ca9f3887f3a49ba72601e206e66050b8a555e129","source":{"kind":"arxiv","id":"2311.03414","version":1},"attestation_state":"computed","paper":{"title":"A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Christoph Petroll, Oliver Niggemann, Philipp Hoefer, Sebastian Eilermann","submitted_at":"2023-11-06T09:33:56Z","abstract_excerpt":"One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the functionalities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered"},"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":"2311.03414","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-06T09:33:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d1112a290a13a525b8c2d5ea7ef96248109d4473a820598c90da2b7ff2ae621d","abstract_canon_sha256":"230db74b8ee610e6a70fe261b830140093bb369b351779e43806f47d84cc8159"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:58.021356Z","signature_b64":"ZXwvRUMvqG+ehdXp64G9ig1PZrNAO+BUOce/pocfmUXukIbucubXhd5+xawDLhics2dtJsffDfhrtV49UEvzDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e7c46cc248accbcccf52a924ca9f3887f3a49ba72601e206e66050b8a555e129","last_reissued_at":"2026-07-05T07:09:58.020877Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:58.020877Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Christoph Petroll, Oliver Niggemann, Philipp Hoefer, Sebastian Eilermann","submitted_at":"2023-11-06T09:33:56Z","abstract_excerpt":"One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the functionalities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.03414","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/2311.03414/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":"2311.03414","created_at":"2026-07-05T07:09:58.020930+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.03414v1","created_at":"2026-07-05T07:09:58.020930+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.03414","created_at":"2026-07-05T07:09:58.020930+00:00"},{"alias_kind":"pith_short_12","alias_value":"47CGZQSIVTF4","created_at":"2026-07-05T07:09:58.020930+00:00"},{"alias_kind":"pith_short_16","alias_value":"47CGZQSIVTF4ZT2S","created_at":"2026-07-05T07:09:58.020930+00:00"},{"alias_kind":"pith_short_8","alias_value":"47CGZQSI","created_at":"2026-07-05T07:09:58.020930+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.01492","citing_title":"A Continuous-Time Consistency Model for 3D Point Cloud Generation","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7","json":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7.json","graph_json":"https://pith.science/api/pith-number/47CGZQSIVTF4ZT2SVESMVHZYQ7/graph.json","events_json":"https://pith.science/api/pith-number/47CGZQSIVTF4ZT2SVESMVHZYQ7/events.json","paper":"https://pith.science/paper/47CGZQSI"},"agent_actions":{"view_html":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7","download_json":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7.json","view_paper":"https://pith.science/paper/47CGZQSI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.03414&json=true","fetch_graph":"https://pith.science/api/pith-number/47CGZQSIVTF4ZT2SVESMVHZYQ7/graph.json","fetch_events":"https://pith.science/api/pith-number/47CGZQSIVTF4ZT2SVESMVHZYQ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7/action/storage_attestation","attest_author":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7/action/author_attestation","sign_citation":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7/action/citation_signature","submit_replication":"https://pith.science/pith/47CGZQSIVTF4ZT2SVESMVHZYQ7/action/replication_record"}},"created_at":"2026-07-05T07:09:58.020930+00:00","updated_at":"2026-07-05T07:09:58.020930+00:00"}