{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AMXZ2Z5ZFKGNMOA6CMQZMLKHID","short_pith_number":"pith:AMXZ2Z5Z","schema_version":"1.0","canonical_sha256":"032f9d67b92a8cd6381e1321962d4740e197846a914f9a2bcff4858800bc7a26","source":{"kind":"arxiv","id":"1808.10367","version":1},"attestation_state":"computed","paper":{"title":"Parametric Topology Optimization with Multi-Resolution Finite Element Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NA","authors_text":"Akil Narayan, Robert M. Kirby, Vahid Keshavarzzadeh","submitted_at":"2018-08-30T15:52:08Z","abstract_excerpt":"We present a methodical procedure for topology optimization under uncertainty with multi-resolution finite element models. We use our framework in a bi-fidelity setting where a coarse and a fine mesh corresponding to low- and high-resolution models are available. The inexpensive low-resolution model is used to explore the parameter space and approximate the parameterized high-resolution model and its sensitivity where parameters are considered in both structural load and stiffness. We provide error bounds for bi-fidelity finite element (FE) approximations and their sensitivities and conduct nu"},"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":"1808.10367","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NA","submitted_at":"2018-08-30T15:52:08Z","cross_cats_sorted":[],"title_canon_sha256":"bc5056e338fe2a2f3e14c4a83812f315fb95c1da8297fc3541415f066c771042","abstract_canon_sha256":"07bc97061d353db58d1c9071b78dab597ecd82299d1abdfde6fa3e807df73a62"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:22.339974Z","signature_b64":"zlDGCtIcABg5h4N7HPdH01wA/eIj5RD2mM69nNzT2gmtpdvMm39OluKgjN2ltOzNb70RfMeX4JImYWUNaoTtBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"032f9d67b92a8cd6381e1321962d4740e197846a914f9a2bcff4858800bc7a26","last_reissued_at":"2026-05-17T23:49:22.339295Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:22.339295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parametric Topology Optimization with Multi-Resolution Finite Element Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NA","authors_text":"Akil Narayan, Robert M. Kirby, Vahid Keshavarzzadeh","submitted_at":"2018-08-30T15:52:08Z","abstract_excerpt":"We present a methodical procedure for topology optimization under uncertainty with multi-resolution finite element models. We use our framework in a bi-fidelity setting where a coarse and a fine mesh corresponding to low- and high-resolution models are available. The inexpensive low-resolution model is used to explore the parameter space and approximate the parameterized high-resolution model and its sensitivity where parameters are considered in both structural load and stiffness. We provide error bounds for bi-fidelity finite element (FE) approximations and their sensitivities and conduct nu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.10367","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":""},"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":"1808.10367","created_at":"2026-05-17T23:49:22.339406+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.10367v1","created_at":"2026-05-17T23:49:22.339406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.10367","created_at":"2026-05-17T23:49:22.339406+00:00"},{"alias_kind":"pith_short_12","alias_value":"AMXZ2Z5ZFKGN","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AMXZ2Z5ZFKGNMOA6","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AMXZ2Z5Z","created_at":"2026-05-18T12:32:13.499390+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2605.17568","citing_title":"Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling","ref_index":90,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17568","citing_title":"Structured Neural Marked Point Processes for Interpretable Event Interaction Modeling","ref_index":90,"is_internal_anchor":true},{"citing_arxiv_id":"2605.01226","citing_title":"Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events","ref_index":77,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID","json":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID.json","graph_json":"https://pith.science/api/pith-number/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/graph.json","events_json":"https://pith.science/api/pith-number/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/events.json","paper":"https://pith.science/paper/AMXZ2Z5Z"},"agent_actions":{"view_html":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID","download_json":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID.json","view_paper":"https://pith.science/paper/AMXZ2Z5Z","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.10367&json=true","fetch_graph":"https://pith.science/api/pith-number/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/graph.json","fetch_events":"https://pith.science/api/pith-number/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/action/storage_attestation","attest_author":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/action/author_attestation","sign_citation":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/action/citation_signature","submit_replication":"https://pith.science/pith/AMXZ2Z5ZFKGNMOA6CMQZMLKHID/action/replication_record"}},"created_at":"2026-05-17T23:49:22.339406+00:00","updated_at":"2026-05-17T23:49:22.339406+00:00"}