{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6WII4JX6GZ2PVULBYERRR4AD3Z","short_pith_number":"pith:6WII4JX6","schema_version":"1.0","canonical_sha256":"f5908e26fe3674fad161c12318f003de4b32263a7e230be50b39c1c483479a70","source":{"kind":"arxiv","id":"2508.05624","version":1},"attestation_state":"computed","paper":{"title":"Latent Space Diffusion for Topology Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Aaron Lutheran, Alireza Tabarraei, Srijan Das","submitted_at":"2025-08-07T17:58:16Z","abstract_excerpt":"Topology optimization enables the automated design of efficient structures by optimally distributing material within a defined domain. However, traditional gradient-based methods often scale poorly with increasing resolution and dimensionality due to the need for repeated finite element analyses and sensitivity evaluations. In this work, we propose a novel framework that combines latent diffusion models (LDMs) with variational autoencoders (VAEs) to enable fast, conditional generation of optimized topologies. Unlike prior approaches, our method conditions the generative process on physically m"},"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":"2508.05624","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2025-08-07T17:58:16Z","cross_cats_sorted":[],"title_canon_sha256":"84fc9f7bd09e59a6ebc9401b0e64c0c44c0cb0fdbfc04c3b708e6315e4234f35","abstract_canon_sha256":"dc00cde9260ec12608b1e049e33f5dafaf8904f2fe2aa612ae409002ef9f2e28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:22.791460Z","signature_b64":"Bo2aOoNIN0jafN+klrC4e4nF5KhHh7n4zyTrrTKJlAuP4Y5I33wY0n/F65G+Gtf1rqerCZ6i1hopipitjQNVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5908e26fe3674fad161c12318f003de4b32263a7e230be50b39c1c483479a70","last_reissued_at":"2026-07-05T11:50:22.790904Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:22.790904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Latent Space Diffusion for Topology Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Aaron Lutheran, Alireza Tabarraei, Srijan Das","submitted_at":"2025-08-07T17:58:16Z","abstract_excerpt":"Topology optimization enables the automated design of efficient structures by optimally distributing material within a defined domain. However, traditional gradient-based methods often scale poorly with increasing resolution and dimensionality due to the need for repeated finite element analyses and sensitivity evaluations. In this work, we propose a novel framework that combines latent diffusion models (LDMs) with variational autoencoders (VAEs) to enable fast, conditional generation of optimized topologies. Unlike prior approaches, our method conditions the generative process on physically m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.05624","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/2508.05624/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":"2508.05624","created_at":"2026-07-05T11:50:22.790966+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.05624v1","created_at":"2026-07-05T11:50:22.790966+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.05624","created_at":"2026-07-05T11:50:22.790966+00:00"},{"alias_kind":"pith_short_12","alias_value":"6WII4JX6GZ2P","created_at":"2026-07-05T11:50:22.790966+00:00"},{"alias_kind":"pith_short_16","alias_value":"6WII4JX6GZ2PVULB","created_at":"2026-07-05T11:50:22.790966+00:00"},{"alias_kind":"pith_short_8","alias_value":"6WII4JX6","created_at":"2026-07-05T11:50:22.790966+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.03522","citing_title":"Physics-Informed Transformer for Real-Time High-Fidelity Topology Optimization","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z","json":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z.json","graph_json":"https://pith.science/api/pith-number/6WII4JX6GZ2PVULBYERRR4AD3Z/graph.json","events_json":"https://pith.science/api/pith-number/6WII4JX6GZ2PVULBYERRR4AD3Z/events.json","paper":"https://pith.science/paper/6WII4JX6"},"agent_actions":{"view_html":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z","download_json":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z.json","view_paper":"https://pith.science/paper/6WII4JX6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.05624&json=true","fetch_graph":"https://pith.science/api/pith-number/6WII4JX6GZ2PVULBYERRR4AD3Z/graph.json","fetch_events":"https://pith.science/api/pith-number/6WII4JX6GZ2PVULBYERRR4AD3Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z/action/storage_attestation","attest_author":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z/action/author_attestation","sign_citation":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z/action/citation_signature","submit_replication":"https://pith.science/pith/6WII4JX6GZ2PVULBYERRR4AD3Z/action/replication_record"}},"created_at":"2026-07-05T11:50:22.790966+00:00","updated_at":"2026-07-05T11:50:22.790966+00:00"}