Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2502.00782.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T22:39:13.590127Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T15:47:06.078210Z
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 f84317d8-46b1-4da6-bc3f-3f175c7b2949 · inbound
IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation cites this paper.
IP-Basis PINNs: Efficient Multi-Query Inverse Parameter Estimation Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T22:39:13.590127Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b2b276a-0ca8-4a12-9b95-c4a86c7cf889 · inbound
Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion cites this paper.
Physics-Informed Neural Networks in Clean Combustion: A Pathway to Sustainable Aerospace Propulsion Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Reference 108
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T21:21:41.279652Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 481dfa01-0518-48dc-ab64-812bf987895c · inbound
Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks cites this paper.
Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T15:47:06.079762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.