{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PSXYRGKT3E7EH76W45H6Q7HCFQ","short_pith_number":"pith:PSXYRGKT","schema_version":"1.0","canonical_sha256":"7caf889953d93e43ffd6e74fe87ce22c2a32b470a19ae3093f841d54db4532d3","source":{"kind":"arxiv","id":"2607.07233","version":1},"attestation_state":"computed","paper":{"title":"HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Boyuan Zhang, Jinbo Yang, Mingyue Yuan, Shikai Jing, Yoshifumi Kitamura","submitted_at":"2026-07-08T10:15:20Z","abstract_excerpt":"Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration. However, these approaches lack intrinsic physics guidance, often leading to poor generalizability across unseen boundary conditions and the formation of floating material artifacts. To address these limitations, we propose Hierarchical Physics-Guided Diffusion (HPG-Diff), a novel diffusion framework that enforces physics consistency through two synergistic mechanisms. First, we introduce a hierarchical physics-guided strategy that aligns different precomputed physics features with th"},"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":"2607.07233","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-08T10:15:20Z","cross_cats_sorted":["cs.CE"],"title_canon_sha256":"ece96d5a59f17f5c1435c3e4637ad8bfe7d42ccc42cc2bf680b63a09d86f0ed7","abstract_canon_sha256":"2d463e5e62fd5617f77dcdf8044ef519d501147dcb16c90afbda92c892182a01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T01:20:18.437103Z","signature_b64":"CiCl+bfp+phwbtKiQmVxcYaO/DSwocstEBsMv2HBCb2jhaf/575TFdnIxsQg8wbPh0j3HkRidG0aAyuPkrU5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7caf889953d93e43ffd6e74fe87ce22c2a32b470a19ae3093f841d54db4532d3","last_reissued_at":"2026-07-09T01:20:18.434263Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T01:20:18.434263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CE"],"primary_cat":"cs.LG","authors_text":"Boyuan Zhang, Jinbo Yang, Mingyue Yuan, Shikai Jing, Yoshifumi Kitamura","submitted_at":"2026-07-08T10:15:20Z","abstract_excerpt":"Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration. However, these approaches lack intrinsic physics guidance, often leading to poor generalizability across unseen boundary conditions and the formation of floating material artifacts. To address these limitations, we propose Hierarchical Physics-Guided Diffusion (HPG-Diff), a novel diffusion framework that enforces physics consistency through two synergistic mechanisms. First, we introduce a hierarchical physics-guided strategy that aligns different precomputed physics features with th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.07233","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/2607.07233/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":"2607.07233","created_at":"2026-07-09T01:20:18.434417+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.07233v1","created_at":"2026-07-09T01:20:18.434417+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.07233","created_at":"2026-07-09T01:20:18.434417+00:00"},{"alias_kind":"pith_short_12","alias_value":"PSXYRGKT3E7E","created_at":"2026-07-09T01:20:18.434417+00:00"},{"alias_kind":"pith_short_16","alias_value":"PSXYRGKT3E7EH76W","created_at":"2026-07-09T01:20:18.434417+00:00"},{"alias_kind":"pith_short_8","alias_value":"PSXYRGKT","created_at":"2026-07-09T01:20:18.434417+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ","json":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ.json","graph_json":"https://pith.science/api/pith-number/PSXYRGKT3E7EH76W45H6Q7HCFQ/graph.json","events_json":"https://pith.science/api/pith-number/PSXYRGKT3E7EH76W45H6Q7HCFQ/events.json","paper":"https://pith.science/paper/PSXYRGKT"},"agent_actions":{"view_html":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ","download_json":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ.json","view_paper":"https://pith.science/paper/PSXYRGKT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.07233&json=true","fetch_graph":"https://pith.science/api/pith-number/PSXYRGKT3E7EH76W45H6Q7HCFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/PSXYRGKT3E7EH76W45H6Q7HCFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ/action/storage_attestation","attest_author":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ/action/author_attestation","sign_citation":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ/action/citation_signature","submit_replication":"https://pith.science/pith/PSXYRGKT3E7EH76W45H6Q7HCFQ/action/replication_record"}},"created_at":"2026-07-09T01:20:18.434417+00:00","updated_at":"2026-07-09T01:20:18.434417+00:00"}