{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:E53ZKBYAKHHUSITBYSINLWOOMK","short_pith_number":"pith:E53ZKBYA","canonical_record":{"source":{"id":"1907.01490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T16:51:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8146b96ea091d1eedb794481833bdc5f96a58966b3b6c0a29e009d514665e30c","abstract_canon_sha256":"cdd81171cd99c89235fa66268aef689e148e1f2d42b76bcfb2ff4a00afc407cf"},"schema_version":"1.0"},"canonical_sha256":"277795070051cf492261c490d5d9ce62b404658f909a483eeaef7907014e084b","source":{"kind":"arxiv","id":"1907.01490","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01490","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01490v1","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01490","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"pith_short_12","alias_value":"E53ZKBYAKHHU","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"E53ZKBYAKHHUSITB","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"E53ZKBYA","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:E53ZKBYAKHHUSITBYSINLWOOMK","target":"record","payload":{"canonical_record":{"source":{"id":"1907.01490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T16:51:40Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8146b96ea091d1eedb794481833bdc5f96a58966b3b6c0a29e009d514665e30c","abstract_canon_sha256":"cdd81171cd99c89235fa66268aef689e148e1f2d42b76bcfb2ff4a00afc407cf"},"schema_version":"1.0"},"canonical_sha256":"277795070051cf492261c490d5d9ce62b404658f909a483eeaef7907014e084b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:40.259542Z","signature_b64":"tpKFBNfbcwH718tlbtQDFAGO3UclmVXjdG4jZdJTaOecL5nuqU/aoJTEOvTT6PrJftAnJ0rUTVDILrd97h9RBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"277795070051cf492261c490d5d9ce62b404658f909a483eeaef7907014e084b","last_reissued_at":"2026-05-17T23:41:40.258951Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:40.258951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.01490","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TCnOND995Ymwrk8HpEBp28IyFL7c46K1zv0bExWYwUo83iWWTFG+rZFGcHi3nkWo425AMb6KTYjWIwC1e8bmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T08:20:26.865175Z"},"content_sha256":"71011cec293f2d1d9811da4c16d0ee274dcd302dd478217e58b894ea6bc69c40","schema_version":"1.0","event_id":"sha256:71011cec293f2d1d9811da4c16d0ee274dcd302dd478217e58b894ea6bc69c40"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:E53ZKBYAKHHUSITBYSINLWOOMK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An innovative adaptive kriging approach for efficient binary classification of mechanical problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amelie Fau, Jan N. Fuhg","submitted_at":"2019-07-02T16:51:40Z","abstract_excerpt":"Kriging is an efficient machine-learning tool, which allows to obtain an approximate response of an investigated phenomenon on the whole parametric space. Adaptive schemes provide a the ability to guide the experiment yielding new sample point positions to enrich the metamodel. Herein a novel adaptive scheme called Monte Carlo-intersite Voronoi (MiVor) is proposed to efficiently identify binary decision regions on the basis of a regression surrogate model. The performance of the innovative approach is tested for analytical functions as well as some mechanical problems and is furthermore compar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01490","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7GBQAV0xEV0TbkQIgu+Y86Z7RJsFdFB9yjpc7CzM7daIhSnYrCMseaJ8ielqN1Adh+vpF7WojXKj4Vxu/o+pAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T08:20:26.865526Z"},"content_sha256":"cf0e3ff1ce372eb0e1a27e36848aa20abd83bc6177fd32f6721ca8c5d195b53d","schema_version":"1.0","event_id":"sha256:cf0e3ff1ce372eb0e1a27e36848aa20abd83bc6177fd32f6721ca8c5d195b53d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E53ZKBYAKHHUSITBYSINLWOOMK/bundle.json","state_url":"https://pith.science/pith/E53ZKBYAKHHUSITBYSINLWOOMK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E53ZKBYAKHHUSITBYSINLWOOMK/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":"2026-05-31T08:20:26Z","links":{"resolver":"https://pith.science/pith/E53ZKBYAKHHUSITBYSINLWOOMK","bundle":"https://pith.science/pith/E53ZKBYAKHHUSITBYSINLWOOMK/bundle.json","state":"https://pith.science/pith/E53ZKBYAKHHUSITBYSINLWOOMK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E53ZKBYAKHHUSITBYSINLWOOMK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:E53ZKBYAKHHUSITBYSINLWOOMK","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":"cdd81171cd99c89235fa66268aef689e148e1f2d42b76bcfb2ff4a00afc407cf","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T16:51:40Z","title_canon_sha256":"8146b96ea091d1eedb794481833bdc5f96a58966b3b6c0a29e009d514665e30c"},"schema_version":"1.0","source":{"id":"1907.01490","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01490","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01490v1","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01490","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"pith_short_12","alias_value":"E53ZKBYAKHHU","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"E53ZKBYAKHHUSITB","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"E53ZKBYA","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:cf0e3ff1ce372eb0e1a27e36848aa20abd83bc6177fd32f6721ca8c5d195b53d","target":"graph","created_at":"2026-05-17T23:41:40Z","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"},"paper":{"abstract_excerpt":"Kriging is an efficient machine-learning tool, which allows to obtain an approximate response of an investigated phenomenon on the whole parametric space. Adaptive schemes provide a the ability to guide the experiment yielding new sample point positions to enrich the metamodel. Herein a novel adaptive scheme called Monte Carlo-intersite Voronoi (MiVor) is proposed to efficiently identify binary decision regions on the basis of a regression surrogate model. The performance of the innovative approach is tested for analytical functions as well as some mechanical problems and is furthermore compar","authors_text":"Amelie Fau, Jan N. Fuhg","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T16:51:40Z","title":"An innovative adaptive kriging approach for efficient binary classification of mechanical problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01490","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":"sha256:71011cec293f2d1d9811da4c16d0ee274dcd302dd478217e58b894ea6bc69c40","target":"record","created_at":"2026-05-17T23:41:40Z","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":"cdd81171cd99c89235fa66268aef689e148e1f2d42b76bcfb2ff4a00afc407cf","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T16:51:40Z","title_canon_sha256":"8146b96ea091d1eedb794481833bdc5f96a58966b3b6c0a29e009d514665e30c"},"schema_version":"1.0","source":{"id":"1907.01490","kind":"arxiv","version":1}},"canonical_sha256":"277795070051cf492261c490d5d9ce62b404658f909a483eeaef7907014e084b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"277795070051cf492261c490d5d9ce62b404658f909a483eeaef7907014e084b","first_computed_at":"2026-05-17T23:41:40.258951Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:40.258951Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tpKFBNfbcwH718tlbtQDFAGO3UclmVXjdG4jZdJTaOecL5nuqU/aoJTEOvTT6PrJftAnJ0rUTVDILrd97h9RBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:40.259542Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.01490","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71011cec293f2d1d9811da4c16d0ee274dcd302dd478217e58b894ea6bc69c40","sha256:cf0e3ff1ce372eb0e1a27e36848aa20abd83bc6177fd32f6721ca8c5d195b53d"],"state_sha256":"097a0d864080cd4f763b979e46b4b75f57a9d41e00abd0c35ad328d8656202ca"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QZ6PcAgXevXY5uj83MXTWsUevzOL7gzjFGSqAiORDo77ng37ksRKDRaJ+ZXp00npgiRmPgmaHmfV6sx8ILuZCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T08:20:26.867296Z","bundle_sha256":"f847482e4e7fab70d82250f392f4589441203c4988707615f60acf0784e65978"}}