{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:E46GDZYWDHFT7PE3VYO53X33PT","short_pith_number":"pith:E46GDZYW","canonical_record":{"source":{"id":"1505.06889","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2015-05-26T10:20:30Z","cross_cats_sorted":["cond-mat.stat-mech","physics.chem-ph","physics.comp-ph"],"title_canon_sha256":"b8e04db48d3cf54cc535d8f9c171eaffa908460dc5243b66e9ce4fc47e9ade94","abstract_canon_sha256":"08c9f3768cac0e0790517c91807f0ef641ce19e922e3d1d33b12dcbc90ad0287"},"schema_version":"1.0"},"canonical_sha256":"273c61e71619cb3fbc9bae1ddddf7b7cfa8d49709c0fb4b99135fbd06bb60ac8","source":{"kind":"arxiv","id":"1505.06889","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.06889","created_at":"2026-05-18T01:19:35Z"},{"alias_kind":"arxiv_version","alias_value":"1505.06889v2","created_at":"2026-05-18T01:19:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.06889","created_at":"2026-05-18T01:19:35Z"},{"alias_kind":"pith_short_12","alias_value":"E46GDZYWDHFT","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"E46GDZYWDHFT7PE3","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"E46GDZYW","created_at":"2026-05-18T12:29:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:E46GDZYWDHFT7PE3VYO53X33PT","target":"record","payload":{"canonical_record":{"source":{"id":"1505.06889","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2015-05-26T10:20:30Z","cross_cats_sorted":["cond-mat.stat-mech","physics.chem-ph","physics.comp-ph"],"title_canon_sha256":"b8e04db48d3cf54cc535d8f9c171eaffa908460dc5243b66e9ce4fc47e9ade94","abstract_canon_sha256":"08c9f3768cac0e0790517c91807f0ef641ce19e922e3d1d33b12dcbc90ad0287"},"schema_version":"1.0"},"canonical_sha256":"273c61e71619cb3fbc9bae1ddddf7b7cfa8d49709c0fb4b99135fbd06bb60ac8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:19:35.362911Z","signature_b64":"x1FEOjA9p/WV/016v0KbhR5nNClIXeqf9og4GENGZtqC0sw7mEa7CfIvb4ovTgmoQyGOn+aBWkMGKyoABuxnBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"273c61e71619cb3fbc9bae1ddddf7b7cfa8d49709c0fb4b99135fbd06bb60ac8","last_reissued_at":"2026-05-18T01:19:35.362446Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:19:35.362446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1505.06889","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:19:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IpuvN7+NbUyAEjDk6UikAcXP5Lspri1yB5tz7B7zPvQZFe/TtZLItt8vmoYk2wYhsH9AV4Zl72+riMHxO7XzDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T11:31:40.930024Z"},"content_sha256":"db4046971ee3fe239f9e41208171d6228630a5664867b239527bdbcc4ef9a428","schema_version":"1.0","event_id":"sha256:db4046971ee3fe239f9e41208171d6228630a5664867b239527bdbcc4ef9a428"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:E46GDZYWDHFT7PE3VYO53X33PT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Thermostats for Noisy Gradient Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","physics.chem-ph","physics.comp-ph"],"primary_cat":"math.NA","authors_text":"Benedict Leimkuhler, Xiaocheng Shang","submitted_at":"2015-05-26T10:20:30Z","abstract_excerpt":"We study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochastic dynamical methods are discussed which have application to multiscale models, nonequilibrium molecular dynamics, and Bayesian sampling techniques arising in emerging machine learning applications. In addition to providing a more comprehensive discussion of the foundations of these methods, we propose a new numerical method for the adaptive Langevin/stochastic gradient Nos\\'{e}--Hoover thermostat that achieves a dra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.06889","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:19:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e71jtesnsvhWEhytxyOLmxYZbk8v3RHTwZja/z5GF24VTgNvlYu/zR3/t/K771LrR3PZ5CfZBD1Spn6J08e4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T11:31:40.930382Z"},"content_sha256":"18511659bf43659179185b1b73754622dda93e69bd9664c21e31f4a38f877323","schema_version":"1.0","event_id":"sha256:18511659bf43659179185b1b73754622dda93e69bd9664c21e31f4a38f877323"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E46GDZYWDHFT7PE3VYO53X33PT/bundle.json","state_url":"https://pith.science/pith/E46GDZYWDHFT7PE3VYO53X33PT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E46GDZYWDHFT7PE3VYO53X33PT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-26T11:31:40Z","links":{"resolver":"https://pith.science/pith/E46GDZYWDHFT7PE3VYO53X33PT","bundle":"https://pith.science/pith/E46GDZYWDHFT7PE3VYO53X33PT/bundle.json","state":"https://pith.science/pith/E46GDZYWDHFT7PE3VYO53X33PT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E46GDZYWDHFT7PE3VYO53X33PT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:E46GDZYWDHFT7PE3VYO53X33PT","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":"08c9f3768cac0e0790517c91807f0ef641ce19e922e3d1d33b12dcbc90ad0287","cross_cats_sorted":["cond-mat.stat-mech","physics.chem-ph","physics.comp-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2015-05-26T10:20:30Z","title_canon_sha256":"b8e04db48d3cf54cc535d8f9c171eaffa908460dc5243b66e9ce4fc47e9ade94"},"schema_version":"1.0","source":{"id":"1505.06889","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.06889","created_at":"2026-05-18T01:19:35Z"},{"alias_kind":"arxiv_version","alias_value":"1505.06889v2","created_at":"2026-05-18T01:19:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.06889","created_at":"2026-05-18T01:19:35Z"},{"alias_kind":"pith_short_12","alias_value":"E46GDZYWDHFT","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_16","alias_value":"E46GDZYWDHFT7PE3","created_at":"2026-05-18T12:29:17Z"},{"alias_kind":"pith_short_8","alias_value":"E46GDZYW","created_at":"2026-05-18T12:29:17Z"}],"graph_snapshots":[{"event_id":"sha256:18511659bf43659179185b1b73754622dda93e69bd9664c21e31f4a38f877323","target":"graph","created_at":"2026-05-18T01:19:35Z","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"},"paper":{"abstract_excerpt":"We study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochastic dynamical methods are discussed which have application to multiscale models, nonequilibrium molecular dynamics, and Bayesian sampling techniques arising in emerging machine learning applications. In addition to providing a more comprehensive discussion of the foundations of these methods, we propose a new numerical method for the adaptive Langevin/stochastic gradient Nos\\'{e}--Hoover thermostat that achieves a dra","authors_text":"Benedict Leimkuhler, Xiaocheng Shang","cross_cats":["cond-mat.stat-mech","physics.chem-ph","physics.comp-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2015-05-26T10:20:30Z","title":"Adaptive Thermostats for Noisy Gradient Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.06889","kind":"arxiv","version":2},"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:db4046971ee3fe239f9e41208171d6228630a5664867b239527bdbcc4ef9a428","target":"record","created_at":"2026-05-18T01:19:35Z","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":"08c9f3768cac0e0790517c91807f0ef641ce19e922e3d1d33b12dcbc90ad0287","cross_cats_sorted":["cond-mat.stat-mech","physics.chem-ph","physics.comp-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2015-05-26T10:20:30Z","title_canon_sha256":"b8e04db48d3cf54cc535d8f9c171eaffa908460dc5243b66e9ce4fc47e9ade94"},"schema_version":"1.0","source":{"id":"1505.06889","kind":"arxiv","version":2}},"canonical_sha256":"273c61e71619cb3fbc9bae1ddddf7b7cfa8d49709c0fb4b99135fbd06bb60ac8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"273c61e71619cb3fbc9bae1ddddf7b7cfa8d49709c0fb4b99135fbd06bb60ac8","first_computed_at":"2026-05-18T01:19:35.362446Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:19:35.362446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x1FEOjA9p/WV/016v0KbhR5nNClIXeqf9og4GENGZtqC0sw7mEa7CfIvb4ovTgmoQyGOn+aBWkMGKyoABuxnBA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:19:35.362911Z","signed_message":"canonical_sha256_bytes"},"source_id":"1505.06889","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db4046971ee3fe239f9e41208171d6228630a5664867b239527bdbcc4ef9a428","sha256:18511659bf43659179185b1b73754622dda93e69bd9664c21e31f4a38f877323"],"state_sha256":"4c68d8c8fda0370e487c6964216ce0c743a0d479f3e4025e552fa1a59484f0ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cp3xPDFpdgUzw3b8cl/JWsLD9AhIDbediHIPYD/00B9mX7aPaOmeADWrnXBrLI5HTnxAMJWBLFR3GcA415ceDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T11:31:40.932260Z","bundle_sha256":"dbc35d93b6f7b0c8f83dbac54c6f7afb8194f9b2a029aef295051f6102a2874e"}}