Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2412.03161.
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 2026-08-07T13:02:02.161582Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T18:57:43.148432Z
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 9697a177-ab4b-40e9-b150-349e0ea450c4 · inbound
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery cites this paper.
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:02:02.161582Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 458e7d22-71a6-445f-85fe-ed8631678d8b · inbound
DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems cites this paper.
DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:31:05.690666Z
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 5e7a00c9-7ea5-46c7-8b06-37af7da92559 · inbound
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs cites this paper.
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T18:57:43.150705Z
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.