Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1809.02362.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:29:47.833466Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T12:39:29.014194Z
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 53b780e7-3f1a-4726-8dda-0cfec6fbde21 · inbound
Deep neural network approximation theory for high-dimensional functions cites this paper.
Deep neural network approximation theory for high-dimensional functions A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T12:39:29.018204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 773acfb8-c205-4eb0-85b9-501c54ce97d6 · inbound
Deep Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure cites this paper.
Deep Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:29:47.833466Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.