{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2ZLMASV5T32ZGZ4TOAPI2R7U2H","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8b65b349bd064b9bfa19e33b74f9cb2710e785eeba6f22134efae850fa52db42","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-12-13T11:30:57Z","title_canon_sha256":"eb110c72e37e1b48bd3f5afa4ab97e48099be6c175464713f96fa682ab3a6b57"},"schema_version":"1.0","source":{"id":"2312.08072","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.08072","created_at":"2026-07-05T07:23:40Z"},{"alias_kind":"arxiv_version","alias_value":"2312.08072v1","created_at":"2026-07-05T07:23:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08072","created_at":"2026-07-05T07:23:40Z"},{"alias_kind":"pith_short_12","alias_value":"2ZLMASV5T32Z","created_at":"2026-07-05T07:23:40Z"},{"alias_kind":"pith_short_16","alias_value":"2ZLMASV5T32ZGZ4T","created_at":"2026-07-05T07:23:40Z"},{"alias_kind":"pith_short_8","alias_value":"2ZLMASV5","created_at":"2026-07-05T07:23:40Z"}],"graph_snapshots":[{"event_id":"sha256:c2425068991c9b1057b7971db4b649befb329c1b56a2b5762829af30019c2f4b","target":"graph","created_at":"2026-07-05T07:23:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2312.08072/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stochastic differential equation (SDE in short) solvers find numerous applications across various fields. However, in practical simulations, we usually resort to using Ito-Taylor series-based methods like the Euler-Maruyama method. These methods often suffer from the limitation of fixed time scales and recalculations for different Brownian motions, which lead to computational inefficiency, especially in generative and sampling models. To address these issues, we propose a novel approach: learning a mapping between the solution of SDE and corresponding Brownian motion. This mapping exhibits ver","authors_text":"Jingyuan Li, Wei Liu","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-12-13T11:30:57Z","title":"An approximate operator-based learning method for the numerical solutions of stochastic differential equations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08072","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2ad77287c2e2aba33da306134f560ce39e86c1a48cc064abc2db433cc3931da0","target":"record","created_at":"2026-07-05T07:23:40Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8b65b349bd064b9bfa19e33b74f9cb2710e785eeba6f22134efae850fa52db42","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-12-13T11:30:57Z","title_canon_sha256":"eb110c72e37e1b48bd3f5afa4ab97e48099be6c175464713f96fa682ab3a6b57"},"schema_version":"1.0","source":{"id":"2312.08072","kind":"arxiv","version":1}},"canonical_sha256":"d656c04abd9ef5936793701e8d47f4d1f0f44a04760de1c11c7e86c733fde2f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d656c04abd9ef5936793701e8d47f4d1f0f44a04760de1c11c7e86c733fde2f3","first_computed_at":"2026-07-05T07:23:40.879589Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:23:40.879589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NtBBG+ub+KbzGgKNjKIQCT/y/JyHR5OR7IG1+KAP/K5BfPfYOe8Ak+4S3vRPGKBLoQeSr3GHqcoZsLwdFM6qAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:23:40.879981Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.08072","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ad77287c2e2aba33da306134f560ce39e86c1a48cc064abc2db433cc3931da0","sha256:c2425068991c9b1057b7971db4b649befb329c1b56a2b5762829af30019c2f4b"],"state_sha256":"bb6ec52cd076aff8f4f0b86dd1fdde9a84fcb4b1f6bcefeb0617c82addd78f82"}