Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.08220.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:23:17.737705Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T17:32:42.837682Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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 3412188b-54d6-4feb-a958-6c681b5d3ccd · inbound
LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport cites this paper.
LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:23:17.737705Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d98dc51-a63f-42ba-9afe-3b9a75613b8b · inbound
Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations cites this paper.
Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
Reference 18
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T17:32:42.843863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.