{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RJKATTVI46RGNACPUXIWOKBKZ6","short_pith_number":"pith:RJKATTVI","schema_version":"1.0","canonical_sha256":"8a5409cea8e7a266804fa5d167282acf962983ec768fe75993a45baf9b889855","source":{"kind":"arxiv","id":"2407.12100","version":2},"attestation_state":"computed","paper":{"title":"An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.ME","authors_text":"David J. Eckman, Mohammadmahdi Ghasemloo","submitted_at":"2024-07-16T18:07:32Z","abstract_excerpt":"Using statistical learning methods to analyze stochastic simulation outputs can significantly enhance decision-making by uncovering relationships between different simulated systems and between a system's inputs and outputs. We focus on clustering multivariate empirical distributions of simulation outputs to identify patterns and trade-offs among performance measures. We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster these multivariate empirical distributions. This framework has several important use cases, including anomaly det"},"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":"2407.12100","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-07-16T18:07:32Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"8f0f10c320c74151660f0142f933b18f0e4f0f666544e8a58918de7e04cfa5d5","abstract_canon_sha256":"a7dd6bbb52f2ba649c45ab4175a1981ee21ec38288522117f29b4e0713fe2730"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:06.392165Z","signature_b64":"wYMliRQHIlwwn7/lcXz3Wak4S6E3DEziIFCDSo7v8Jcu1ETQCgYMSiUPdvdmKTfDmD9lA7qRUNb0sJs/1oPvCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a5409cea8e7a266804fa5d167282acf962983ec768fe75993a45baf9b889855","last_reissued_at":"2026-05-28T01:04:06.391491Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:06.391491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.ME","authors_text":"David J. Eckman, Mohammadmahdi Ghasemloo","submitted_at":"2024-07-16T18:07:32Z","abstract_excerpt":"Using statistical learning methods to analyze stochastic simulation outputs can significantly enhance decision-making by uncovering relationships between different simulated systems and between a system's inputs and outputs. We focus on clustering multivariate empirical distributions of simulation outputs to identify patterns and trade-offs among performance measures. We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster these multivariate empirical distributions. This framework has several important use cases, including anomaly det"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.12100","kind":"arxiv","version":2},"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/2407.12100/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":"2407.12100","created_at":"2026-05-28T01:04:06.391569+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.12100v2","created_at":"2026-05-28T01:04:06.391569+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.12100","created_at":"2026-05-28T01:04:06.391569+00:00"},{"alias_kind":"pith_short_12","alias_value":"RJKATTVI46RG","created_at":"2026-05-28T01:04:06.391569+00:00"},{"alias_kind":"pith_short_16","alias_value":"RJKATTVI46RGNACP","created_at":"2026-05-28T01:04:06.391569+00:00"},{"alias_kind":"pith_short_8","alias_value":"RJKATTVI","created_at":"2026-05-28T01:04:06.391569+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/RJKATTVI46RGNACPUXIWOKBKZ6","json":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6.json","graph_json":"https://pith.science/api/pith-number/RJKATTVI46RGNACPUXIWOKBKZ6/graph.json","events_json":"https://pith.science/api/pith-number/RJKATTVI46RGNACPUXIWOKBKZ6/events.json","paper":"https://pith.science/paper/RJKATTVI"},"agent_actions":{"view_html":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6","download_json":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6.json","view_paper":"https://pith.science/paper/RJKATTVI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.12100&json=true","fetch_graph":"https://pith.science/api/pith-number/RJKATTVI46RGNACPUXIWOKBKZ6/graph.json","fetch_events":"https://pith.science/api/pith-number/RJKATTVI46RGNACPUXIWOKBKZ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6/action/storage_attestation","attest_author":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6/action/author_attestation","sign_citation":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6/action/citation_signature","submit_replication":"https://pith.science/pith/RJKATTVI46RGNACPUXIWOKBKZ6/action/replication_record"}},"created_at":"2026-05-28T01:04:06.391569+00:00","updated_at":"2026-05-28T01:04:06.391569+00:00"}