Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1809.07321.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:09:59.958638Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T17:42:25.488371Z
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 32bf5e20-1e7c-42cd-9e2e-784c96337a12 · inbound
Space-time error estimates for deep neural network approximations for differential equations cites this paper.
Space-time error estimates for deep neural network approximations for differential equations A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:09:59.958638Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ecfc1226-a8b1-4783-8d02-f21732d264ff · inbound
Deep neural network approximations for Monte Carlo algorithms cites this paper.
Deep neural network approximations for Monte Carlo algorithms A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:39:29.602442Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d010f582-404f-49a4-8ad2-dec1fec452ec · inbound
FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations cites this paper.
FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T15:31:47.050097Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6741d27-f1bd-4a9a-8634-c63b54788d18 · inbound
Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs cites this paper.
Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-10T17:42:25.496559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.