Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2207.10289.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:59:45.001829Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T16:55:08.393843Z
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 4a9b4062-f831-4bde-b14e-5cf79d1ac98b · inbound
Multi-Fidelity Machine Learning Applied to Steady Fluid Flows cites this paper.
Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:59:45.001829Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8c2fd68-3462-4886-8499-2fa19f2a8742 · inbound
Hierarchical Framework of Runaway Electrons using Deep Learning cites this paper.
Hierarchical Framework of Runaway Electrons using Deep Learning A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T13:38:19.065874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 91608d6a-aed3-40af-9ea2-12278810a458 · inbound
Mass-Conserving Physics-Informed Neural Networks For The One-Dimensional Advection-Diffusion Equation cites this paper.
Mass-Conserving Physics-Informed Neural Networks For The One-Dimensional Advection-Diffusion Equation A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T16:55:08.395238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.