Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.02801.
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 2026-08-12T11:36:52.859086Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T05:46:26.551348Z
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 6bd6a628-4814-4e72-9bc6-8de5ee3b23fb · inbound
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators cites this paper.
Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:36:52.859086Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 935683cb-2ed9-4f03-859a-5e6d551d5ee0 · inbound
Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks cites this paper.
Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Reference 48
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:46:26.564345Z
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.