Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2504.01093.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T00:31:22.354017Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 42abdb5f-2e15-4cd5-8155-1085b558c815 · inbound
AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training cites this paper.
AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:41:23.902199Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 01c38ba0-c899-4844-9cbf-130d7cb87d26 · inbound
Exact Boundary Enforcement Along Implicit Geometries for Physics-Informed, Deep Learning Problems in Continuum Mechanics cites this paper.
Exact Boundary Enforcement Along Implicit Geometries for Physics-Informed, Deep Learning Problems in Continuum Mechanics Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T14:13:29.639631Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 53ed1493-c2d9-4d6d-a5af-d91e6e564f26 · inbound
Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains cites this paper.
Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains Hard-constraining Neumann boundary conditions in physics-informed neural networks via Fourier feature embeddings
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T00:31:22.354017Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.