Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The Random Feature Method for Time-dependent Problems
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2304.06913.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:40:20.718959Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T09:03:15.602321Z
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 915cd7fb-b22b-4a2b-9b7d-4748c4dc18fb · inbound
Adaptive feature capture method for solving partial differential equations with near singular solutions cites this paper.
Adaptive feature capture method for solving partial differential equations with near singular solutions The Random Feature Method for Time-dependent Problems
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:40:20.718959Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 72fdbe28-871e-4897-8bb4-ef6ca580a230 · inbound
CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions cites this paper.
CLINN: Conservation Law Informed Neural Network for Approximating Discontinuous Solutions The Random Feature Method for Time-dependent Problems
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:58:19.993179Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17f7a18f-97a0-47f9-913d-0fed6e9a8547 · inbound
A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge--Kutta Time Stepping cites this paper.
A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge--Kutta Time Stepping The Random Feature Method for Time-dependent Problems
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T00:11:14.261037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation bfd82ec2-634b-40f7-9902-5db68fcbecfe · inbound
A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations cites this paper.
A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations The Random Feature Method for Time-dependent Problems
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T09:03:15.603812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 80194a85-aaff-402d-b71f-0c1e5907e695 · inbound
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs cites this paper.
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs The Random Feature Method for Time-dependent Problems
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T00:02:49.783069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.