Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.00599.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:52.904609Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T12:26:11.484268Z
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 8b25d2cb-9091-4ead-8683-1c13811b4688 · inbound
SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks cites this paper.
SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:15:52.904609Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b18e7b45-75cd-46fe-81af-0402d1995de4 · inbound
Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction cites this paper.
Physics-Informed Neural Networks for High-Precision Grad-Shafranov Equilibrium Reconstruction A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:13:18.380379Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 09aa334f-e814-4518-a78f-09a52e42292f · inbound
Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle cites this paper.
Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:26:11.492010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bca59c9c-f8d2-42bb-9dde-38c0170db92b · inbound
Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks cites this paper.
Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T02:27:16.897595Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.