Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2207.08483.
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 2026-05-08T07:26:22.088809Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T00:21:23.443155Z
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 5e17da4b-5a1c-4775-ba2e-c71c75d86228 · inbound
GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries cites this paper.
GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:01:11.740361Z
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 82978f0d-3415-44d9-b80c-54ba7b08ddf1 · inbound
Learning Neural Operator Surrogates for the Black Hole Accretion Code cites this paper.
Learning Neural Operator Surrogates for the Black Hole Accretion Code wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T00:21:23.516300Z
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.