Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2210.14320.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T11:33:48.247663Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T20:32:37.978805Z
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 cdc5bc58-8559-4f1b-8c06-e6b14863ab6c · inbound
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations cites this paper.
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:33:48.247663Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91146771-3796-4f67-a61a-cfd8fbcd95f4 · inbound
Continuous Data Assimilation with Learned Surrogate Dynamics cites this paper.
Continuous Data Assimilation with Learned Surrogate Dynamics FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T20:32:37.980343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation e0f64dfc-4d65-4ace-a00d-05b512f96dd3 · inbound
Physics-Informed Neural Networks for Discovering Periodic Orbits in the Gravitational Three-Body Problem cites this paper.
Physics-Informed Neural Networks for Discovering Periodic Orbits in the Gravitational Three-Body Problem FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T20:43:13.964970Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.