Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2107.07871.
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 2026-08-04T12:43:36.469540Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T00:16:23.881613Z
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 6130aeec-ed7b-472c-8308-2069210a03e2 · inbound
A physics-informed neural network approach to the point defect model for electrochemical oxide film growth cites this paper.
A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T12:43:36.469540Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf38194b-34db-4c20-b466-388768447ec0 · inbound
When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions cites this paper.
When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:06:14.695237Z
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 f73a3efb-505e-4b32-ad61-188804819c7d · inbound
Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks cites this paper.
Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T00:16:23.884929Z
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 659bbee3-c144-4243-b5ab-031cb2eaab29 · 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 Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T02:27:16.920441Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.