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Paper Citation Record · LEDGER

High precision PINNs in unbounded domains: application to singularity formulation in PDEs

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2506.19243.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.19243 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:51:35.743387Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T15:44:48.269116Z

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 06ea3a16-6b1f-4304-836a-066b4da85947 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms High precision PINNs in unbounded domains: application to singularity formulation in PDEs

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-05-17T01:58:51.563604Z

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.

source=pdf_text observed=2026-05-17T01:55:52.535545Z digest=sha256:0338fbf1223093394b8c8bd3fa4e240fc57d2c3014f485cb1322f63c790d2c92

Observation 0362cd93-b995-4f7d-b715-6f86e5888946 · inbound

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms High precision PINNs in unbounded domains: application to singularity formulation in PDEs

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-03T18:51:35.743387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:51:35.743387Z digest=sha256:e60d7a3bf71db9de6b78daf4f1d537e8af32c1df3012cf800dec546b3d0a60a0

Observation 2ba5796c-3995-4e5c-8593-2e7c7dfa51f9 · inbound

On putative self-similarity for incompressible 3D Euler cites this paper.

On putative self-similarity for incompressible 3D Euler High precision PINNs in unbounded domains: application to singularity formulation in PDEs

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-02T22:22:13.650119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:22:13.650119Z digest=sha256:705852cfb4669f391249325ffb4ada1eaa643da086b25953ad1664ab9b1457e0

Observation ed524071-6704-4b73-aa17-c8d6631f92c9 · inbound

Fourier Feature Pyramids for Physics-Informed Neural Networks cites this paper.

Fourier Feature Pyramids for Physics-Informed Neural Networks High precision PINNs in unbounded domains: application to singularity formulation in PDEs

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:44:48.270794Z

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.

source=pdf_text observed=2026-06-30T15:42:40.028190Z digest=sha256:c8ea47e32eefc355e54635537464d20a7b29dfd78165efe29e85e207e7a62a92

Observation 3cd452fa-f995-4e8e-9449-196a4e93c60c · inbound

Dual Variational Neural Network for the $p$-Laplace Problem cites this paper.

Dual Variational Neural Network for the $p$-Laplace Problem High precision PINNs in unbounded domains: application to singularity formulation in PDEs

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-06-29T11:13:21.162419Z

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.

source=pdf_text observed=2026-06-29T11:04:20.357181Z digest=sha256:6631a3f8b62cd058cda0d0f28fba50309276cf47a7505cfe540ec1e5c5d298d3