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Paper Citation Record · LEDGER

Learning in PINNs: Phase transition, total diffusion, and generalization

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.18494.

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

pith.paper-citation-record.v1
2403.18494 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 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:48:54.820468Z

measured 1 of 1 external citation measurements

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

Source: pith, 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

11
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7a7b1fb2-37ed-4bf2-a02d-2ce5ae4ad2cc · inbound

KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics cites this paper.

KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics Learning in PINNs: Phase transition, total diffusion, and generalization

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-11T10:23:14.210666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:23:14.210666Z digest=sha256:303c18bfa21899e4e5c6b9c61a767535fb1e4023f2f67ac13d47bb1209cfe397

Observation 59b0567f-acb6-46dc-8846-4eec532df011 · inbound

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design cites this paper.

FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design Learning in PINNs: Phase transition, total diffusion, and generalization

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-15T22:48:54.820468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:48:54.820468Z digest=sha256:c75c7ea030ec2a4b5429206d368501fb8e893870c683657fd3d7f2bf3583c6db

Observation 55a95b99-d1a3-4d23-8d82-630443939f0f · inbound

Multi-Resolution Training-Enhanced Kolmogorov-Arnold Networks for Multi-Scale PDE Problems cites this paper.

Multi-Resolution Training-Enhanced Kolmogorov-Arnold Networks for Multi-Scale PDE Problems Learning in PINNs: Phase transition, total diffusion, and generalization

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T13:58:34.090259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:58:34.090259Z digest=sha256:18918af7c79ebf2715d680cfc223ec284b1e97eebf6cd417bf5c8cdc5ed9dd5c

Observation 39b13096-8085-4390-9ae2-1c8e166b5f33 · inbound

Estimating bottom topography in shallow water flows cites this paper.

Estimating bottom topography in shallow water flows Learning in PINNs: Phase transition, total diffusion, and generalization

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-10T18:15:42.508506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T18:10:50.642474Z digest=sha256:393e7cbc2cfe46111c34a93028cb50dec9d3e77e15c7f4f075179a4c9ede4dac

Observation 76608982-fc12-461e-b2aa-0a6826dd04b1 · inbound

Learning Turbulence Closures with Physics-Informed Neural Networks for the Rayleigh-Taylor Transition to Turbulence cites this paper.

Learning Turbulence Closures with Physics-Informed Neural Networks for the Rayleigh-Taylor Transition to Turbulence Learning in PINNs: Phase transition, total diffusion, and generalization

Reference 74

Resolution
verified exact
local_arxiv, observed 2026-07-09T21:46:34.600002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-09T21:43:53.789848Z digest=sha256:5f769634ff65002e1e4992db1f441fb29fb4702a5a5df71e24791835279b6f01