{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:A5DB67KOHATKE7FUYZDKITTKVO","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":"2de1d69610a37e36e5d0132791783191069c0669a58cf3d67143b2aee8cad44a","cross_cats_sorted":["cs.LG","cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MS","submitted_at":"2021-12-03T07:20:50Z","title_canon_sha256":"e5df5ac37ea16965af4adfec19f46a2d31a529a1fb8594695a504ef71e5857ca"},"schema_version":"1.0","source":{"id":"2112.02100","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.02100","created_at":"2026-07-05T03:37:48Z"},{"alias_kind":"arxiv_version","alias_value":"2112.02100v1","created_at":"2026-07-05T03:37:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02100","created_at":"2026-07-05T03:37:48Z"},{"alias_kind":"pith_short_12","alias_value":"A5DB67KOHATK","created_at":"2026-07-05T03:37:48Z"},{"alias_kind":"pith_short_16","alias_value":"A5DB67KOHATKE7FU","created_at":"2026-07-05T03:37:48Z"},{"alias_kind":"pith_short_8","alias_value":"A5DB67KO","created_at":"2026-07-05T03:37:48Z"}],"graph_snapshots":[{"event_id":"sha256:1037ca455f97547724adadb905f14b6a5ff94a4b3e94a970011420420dc3925c","target":"graph","created_at":"2026-07-05T03:37:48Z","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/2112.02100/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential equation simulation. PNMs naturally incorporate prior information about a problem and quantify uncertainty due to finite computational resources as well as stochastic input. In this paper, we present ProbNum: a Python library providing state-of-the-art probabilistic numerical solvers. ProbNum enables custom composition of PNMs for specific problem classes via a modular design as well as wrappers for off-the-shelf us","authors_text":"Alexandra Gessner, Fran\\c{c}ois-Xavier Briol, Johannes Zenn, Jonathan Schmidt, Jonathan Wenger, Maren Mahsereci, Marvin Pf\\\"ortner, Nathanael Bosch, Nicholas Kr\\\"amer, Nina Effenberger, Philipp Hennig, Toni Karvonen","cross_cats":["cs.LG","cs.NA","math.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MS","submitted_at":"2021-12-03T07:20:50Z","title":"ProbNum: Probabilistic Numerics in Python"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02100","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":"sha256:07f3253d3dee741f9637bf2f3286da108c788f1588366fbce8492e79cbdef394","target":"record","created_at":"2026-07-05T03:37:48Z","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":"2de1d69610a37e36e5d0132791783191069c0669a58cf3d67143b2aee8cad44a","cross_cats_sorted":["cs.LG","cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MS","submitted_at":"2021-12-03T07:20:50Z","title_canon_sha256":"e5df5ac37ea16965af4adfec19f46a2d31a529a1fb8594695a504ef71e5857ca"},"schema_version":"1.0","source":{"id":"2112.02100","kind":"arxiv","version":1}},"canonical_sha256":"07461f7d4e3826a27cb4c646a44e6aab8cfaf9c5942c44f53883d14c45e96629","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07461f7d4e3826a27cb4c646a44e6aab8cfaf9c5942c44f53883d14c45e96629","first_computed_at":"2026-07-05T03:37:48.909525Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:37:48.909525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B2DaFzqvXYTN8hTcPeZNlKe2XgAbttU5JfGmL61c9W7SklO/AJc1aWhhx+A5Zyok/ETWE1Ws96Zthb8t7OzJCw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:37:48.909919Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.02100","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:07f3253d3dee741f9637bf2f3286da108c788f1588366fbce8492e79cbdef394","sha256:1037ca455f97547724adadb905f14b6a5ff94a4b3e94a970011420420dc3925c"],"state_sha256":"10d3785bfca5f8b43284179cee9c4981a347845ef43f9d9714cfc2689fe1b3df"}