Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.01547.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T06:07:25.386443Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T03:29:29.699626Z
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 a61f2108-bda3-45d6-975f-64b4c956db49 · inbound
MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems cites this paper.
MCMC-Net: Accelerating Markov Chain Monte Carlo with Neural Networks for Inverse Problems A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T06:07:25.386443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c92abaeb-a7df-4c62-830a-2f9fe24da198 · inbound
Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations cites this paper.
Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T03:29:29.705091Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.