Paper Citation Record · LEDGER

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2208.01565.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2208.01565 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:15:42.277590Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T10:09:45.091849Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7810d043-dd03-49f8-b03d-0e434a9da96d · inbound

Locally Adaptive Conformal Inference for Operator Models cites this paper.

Locally Adaptive Conformal Inference for Operator Models Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T13:15:42.277590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9189fb55-3bad-42b9-935b-f058d55e10e9 · inbound

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime cites this paper.

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:47:25.774141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dbf4c305-1caf-4d02-aa6f-cf9db44c75a7 · inbound

Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning cites this paper.

Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:55.681504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a55ea5b-4ebf-4a34-b2a7-f7a1556e5958 · inbound

Neural Operator Processes for Probabilistic Operator Learning under Partial Observations cites this paper.

Neural Operator Processes for Probabilistic Operator Learning under Partial Observations Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:09:45.093441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.