{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:66MSEVNFIGZYYLLQWQY5K2GAJD","short_pith_number":"pith:66MSEVNF","schema_version":"1.0","canonical_sha256":"f7992255a541b38c2d70b431d568c048de8d617bef6e2e85281bb876f44e63dc","source":{"kind":"arxiv","id":"2404.16896","version":1},"attestation_state":"computed","paper":{"title":"A Neural-Network-Based Approach for Loose-Fitting Clothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.GR","authors_text":"Abishek Kumar, Dalton Omens, Joseph Teran, Kenji Tashiro, Ronald Fedkiw, Yongxu Jin, Zhenglin Geng","submitted_at":"2024-04-25T05:52:20Z","abstract_excerpt":"Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation. Although there is some flexibility in the choice of the numerical algorithm used as a proxy for full simulation, it is essential that the stability and accuracy be independent from any time step restriction or similar requirements in order to facilitate real-time performance. In "},"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":"2404.16896","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.GR","submitted_at":"2024-04-25T05:52:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0bf875514f715e84ce825753c5b782e482c68209879ca1620f94bdfea393eb2a","abstract_canon_sha256":"e743c31ae2ab2554845739d6a3156f8516a86caa603e8c577f40be561f080f8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:16.665759Z","signature_b64":"xg0wo3Vy4B882l2vBeoGDKywg5Rcoa2kLH2KWWEfmfsGR0EKFMu4XdT+/aPxrF74yDOaIWmuc0NXbhWuUa/EDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7992255a541b38c2d70b431d568c048de8d617bef6e2e85281bb876f44e63dc","last_reissued_at":"2026-07-05T08:12:16.665230Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:16.665230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Neural-Network-Based Approach for Loose-Fitting Clothing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.GR","authors_text":"Abishek Kumar, Dalton Omens, Joseph Teran, Kenji Tashiro, Ronald Fedkiw, Yongxu Jin, Zhenglin Geng","submitted_at":"2024-04-25T05:52:20Z","abstract_excerpt":"Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation. Although there is some flexibility in the choice of the numerical algorithm used as a proxy for full simulation, it is essential that the stability and accuracy be independent from any time step restriction or similar requirements in order to facilitate real-time performance. In "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16896","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/2404.16896/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":"2404.16896","created_at":"2026-07-05T08:12:16.665294+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16896v1","created_at":"2026-07-05T08:12:16.665294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16896","created_at":"2026-07-05T08:12:16.665294+00:00"},{"alias_kind":"pith_short_12","alias_value":"66MSEVNFIGZY","created_at":"2026-07-05T08:12:16.665294+00:00"},{"alias_kind":"pith_short_16","alias_value":"66MSEVNFIGZYYLLQ","created_at":"2026-07-05T08:12:16.665294+00:00"},{"alias_kind":"pith_short_8","alias_value":"66MSEVNF","created_at":"2026-07-05T08:12:16.665294+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.15755","citing_title":"Neural Robot Dynamics","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD","json":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD.json","graph_json":"https://pith.science/api/pith-number/66MSEVNFIGZYYLLQWQY5K2GAJD/graph.json","events_json":"https://pith.science/api/pith-number/66MSEVNFIGZYYLLQWQY5K2GAJD/events.json","paper":"https://pith.science/paper/66MSEVNF"},"agent_actions":{"view_html":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD","download_json":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD.json","view_paper":"https://pith.science/paper/66MSEVNF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16896&json=true","fetch_graph":"https://pith.science/api/pith-number/66MSEVNFIGZYYLLQWQY5K2GAJD/graph.json","fetch_events":"https://pith.science/api/pith-number/66MSEVNFIGZYYLLQWQY5K2GAJD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD/action/storage_attestation","attest_author":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD/action/author_attestation","sign_citation":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD/action/citation_signature","submit_replication":"https://pith.science/pith/66MSEVNFIGZYYLLQWQY5K2GAJD/action/replication_record"}},"created_at":"2026-07-05T08:12:16.665294+00:00","updated_at":"2026-07-05T08:12:16.665294+00:00"}