{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:JDQD345QBBSPVLHMHPXZ422I4X","short_pith_number":"pith:JDQD345Q","schema_version":"1.0","canonical_sha256":"48e03df3b00864faacec3bef9e6b48e5fdef1723b98a3d081a401485adc85aa0","source":{"kind":"arxiv","id":"1410.2109","version":1},"attestation_state":"computed","paper":{"title":"Self-Healing Umbrella Sampling: Convergence and efficiency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","stat.CO"],"primary_cat":"math.PR","authors_text":"B. Jourdain, CNRS, Ecole des Ponts, G. Fort (LTCI, G. Stoltz (CERMICS, INRIA Rocquencourt), Telecom Paris Tech), T. Lelievre","submitted_at":"2014-10-08T13:34:45Z","abstract_excerpt":"The Self-Healing Umbrella Sampling (SHUS) algorithm is an adaptive biasing algorithm which has been proposed to efficiently sample a multimodal probability measure. We show that this method can be seen as a variant of the well-known Wang-Landau algorithm. Adapting results on the convergence of the Wang-Landau algorithm, we prove the convergence of the SHUS algorithm. We also compare the two methods in terms of efficiency. We finally propose a modification of the SHUS algorithm in order to increase its efficiency, and exhibit some similarities of SHUS with the well-tempered metadynamics method."},"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":"1410.2109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.PR","submitted_at":"2014-10-08T13:34:45Z","cross_cats_sorted":["cond-mat.stat-mech","stat.CO"],"title_canon_sha256":"0f3c02f53afe714d1b2f910051e405f118f5450d567c8527717a5ff76cceb8e7","abstract_canon_sha256":"a0c7fe359d2b3223b623bcdf5df2c12fa62715df4449416ec8a644381eaec24b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:48.202433Z","signature_b64":"yw9SXAFvYPS7K/NGx+xiauWdIISxqeUehRRC1+ytQD0eIetJtEOEs3E46TkUQmOwkVY7xHAJGaXtU5JUsVPPDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48e03df3b00864faacec3bef9e6b48e5fdef1723b98a3d081a401485adc85aa0","last_reissued_at":"2026-05-18T02:40:48.201935Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:48.201935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Healing Umbrella Sampling: Convergence and efficiency","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","stat.CO"],"primary_cat":"math.PR","authors_text":"B. Jourdain, CNRS, Ecole des Ponts, G. Fort (LTCI, G. Stoltz (CERMICS, INRIA Rocquencourt), Telecom Paris Tech), T. Lelievre","submitted_at":"2014-10-08T13:34:45Z","abstract_excerpt":"The Self-Healing Umbrella Sampling (SHUS) algorithm is an adaptive biasing algorithm which has been proposed to efficiently sample a multimodal probability measure. We show that this method can be seen as a variant of the well-known Wang-Landau algorithm. Adapting results on the convergence of the Wang-Landau algorithm, we prove the convergence of the SHUS algorithm. We also compare the two methods in terms of efficiency. We finally propose a modification of the SHUS algorithm in order to increase its efficiency, and exhibit some similarities of SHUS with the well-tempered metadynamics method."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1410.2109","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":"1410.2109","created_at":"2026-05-18T02:40:48.202004+00:00"},{"alias_kind":"arxiv_version","alias_value":"1410.2109v1","created_at":"2026-05-18T02:40:48.202004+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1410.2109","created_at":"2026-05-18T02:40:48.202004+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDQD345QBBSP","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDQD345QBBSPVLHM","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDQD345Q","created_at":"2026-05-18T12:28:33.132498+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/JDQD345QBBSPVLHMHPXZ422I4X","json":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X.json","graph_json":"https://pith.science/api/pith-number/JDQD345QBBSPVLHMHPXZ422I4X/graph.json","events_json":"https://pith.science/api/pith-number/JDQD345QBBSPVLHMHPXZ422I4X/events.json","paper":"https://pith.science/paper/JDQD345Q"},"agent_actions":{"view_html":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X","download_json":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X.json","view_paper":"https://pith.science/paper/JDQD345Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1410.2109&json=true","fetch_graph":"https://pith.science/api/pith-number/JDQD345QBBSPVLHMHPXZ422I4X/graph.json","fetch_events":"https://pith.science/api/pith-number/JDQD345QBBSPVLHMHPXZ422I4X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X/action/storage_attestation","attest_author":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X/action/author_attestation","sign_citation":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X/action/citation_signature","submit_replication":"https://pith.science/pith/JDQD345QBBSPVLHMHPXZ422I4X/action/replication_record"}},"created_at":"2026-05-18T02:40:48.202004+00:00","updated_at":"2026-05-18T02:40:48.202004+00:00"}