Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.00573.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T14:49:00.476226Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T08:54:48.836832Z
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 2cb46781-6a41-4457-9d95-d86ccdc58040 · inbound
A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms cites this paper.
A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:49:00.476226Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a55e99b0-a210-4399-86ae-1e65c124e37d · inbound
Feature Learning for the High Dimensional Stationary Sch\"odinger Equation with Deep Ritz Method cites this paper.
Feature Learning for the High Dimensional Stationary Sch\"odinger Equation with Deep Ritz Method Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
Reference 59
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T08:54:48.838570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a402d550-22df-4de6-8893-982de62af603 · inbound
The Differential Neural Tangent Kernel and Its Positivity cites this paper.
The Differential Neural Tangent Kernel and Its Positivity Convergence Analysis of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T13:34:45.196896Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.