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Paper Citation Record · LEDGER

A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.13361.

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

pith.paper-citation-record.v1
2110.13361 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:59:44.938018Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:23:52.281166Z

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 fb42c71d-de44-4de6-a65d-9bb9e188f71b · inbound

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows cites this paper.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-10T14:59:44.938018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:59:44.938018Z digest=sha256:dc64d59c633db97c924b7bd9fe9db37ef573804c545e2c155dd2556e7760acb9

Observation 93b48186-03d8-4b4a-bb0a-e1fffc133570 · inbound

S$^2$GPT-PINNs: Sparse and Small models for PDEs cites this paper.

S$^2$GPT-PINNs: Sparse and Small models for PDEs A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs

Reference 30

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T14:23:52.360309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-07T14:23:51.825707Z digest=sha256:8150fffa4b5ddb2cc8e6c93176e6510f344ed3faff51ce2188db40fd381c4e36