{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IR43BI4N6YEB6AQEKPAP5ZWZHR","short_pith_number":"pith:IR43BI4N","schema_version":"1.0","canonical_sha256":"4479b0a38df6081f020453c0fee6d93c6f8a1956f7e245d25fc334707c9147eb","source":{"kind":"arxiv","id":"2503.15561","version":1},"attestation_state":"computed","paper":{"title":"Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amin Yousefpour, Ramin Bostanabad, Shirin Hosseinmardi, Xiangyu Sun","submitted_at":"2025-03-18T22:59:16Z","abstract_excerpt":"We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs). Our framework endows all design and state variables via GP priors which have a shared, multi-output mean function that is parametrized via a customized deep neural network (DNN). The parameters of this mean function are estimated by minimizing a multi-component loss function that depends on the performance metric, design constraints, and the residuals on the state equations. Our TO approach yields well-defined material interfaces and has a built-in continuation natu"},"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":"2503.15561","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-03-18T22:59:16Z","cross_cats_sorted":[],"title_canon_sha256":"dea386fb8ec5f4d09fda65d438bdb02ecf1166e0b9a07df6df7fd101ec99a0e3","abstract_canon_sha256":"8bdaf0430cf7bdc914e3f15b647ed596e61056e8332d0f78825345b7052a27fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:35:52.714471Z","signature_b64":"B9xAJiL1B8snqqUoIi2J0wQtSuD+JwlzqcjhudteH+w10Yqb8AWlX5m6ntjh9bHX5+A5V3gws+RHO9pxuSHNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4479b0a38df6081f020453c0fee6d93c6f8a1956f7e245d25fc334707c9147eb","last_reissued_at":"2026-07-05T10:35:52.713941Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:35:52.713941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amin Yousefpour, Ramin Bostanabad, Shirin Hosseinmardi, Xiangyu Sun","submitted_at":"2025-03-18T22:59:16Z","abstract_excerpt":"We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs). Our framework endows all design and state variables via GP priors which have a shared, multi-output mean function that is parametrized via a customized deep neural network (DNN). The parameters of this mean function are estimated by minimizing a multi-component loss function that depends on the performance metric, design constraints, and the residuals on the state equations. Our TO approach yields well-defined material interfaces and has a built-in continuation natu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15561","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/2503.15561/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":"2503.15561","created_at":"2026-07-05T10:35:52.714016+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.15561v1","created_at":"2026-07-05T10:35:52.714016+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15561","created_at":"2026-07-05T10:35:52.714016+00:00"},{"alias_kind":"pith_short_12","alias_value":"IR43BI4N6YEB","created_at":"2026-07-05T10:35:52.714016+00:00"},{"alias_kind":"pith_short_16","alias_value":"IR43BI4N6YEB6AQE","created_at":"2026-07-05T10:35:52.714016+00:00"},{"alias_kind":"pith_short_8","alias_value":"IR43BI4N","created_at":"2026-07-05T10:35:52.714016+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/IR43BI4N6YEB6AQEKPAP5ZWZHR","json":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR.json","graph_json":"https://pith.science/api/pith-number/IR43BI4N6YEB6AQEKPAP5ZWZHR/graph.json","events_json":"https://pith.science/api/pith-number/IR43BI4N6YEB6AQEKPAP5ZWZHR/events.json","paper":"https://pith.science/paper/IR43BI4N"},"agent_actions":{"view_html":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR","download_json":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR.json","view_paper":"https://pith.science/paper/IR43BI4N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.15561&json=true","fetch_graph":"https://pith.science/api/pith-number/IR43BI4N6YEB6AQEKPAP5ZWZHR/graph.json","fetch_events":"https://pith.science/api/pith-number/IR43BI4N6YEB6AQEKPAP5ZWZHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR/action/storage_attestation","attest_author":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR/action/author_attestation","sign_citation":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR/action/citation_signature","submit_replication":"https://pith.science/pith/IR43BI4N6YEB6AQEKPAP5ZWZHR/action/replication_record"}},"created_at":"2026-07-05T10:35:52.714016+00:00","updated_at":"2026-07-05T10:35:52.714016+00:00"}