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Paper Citation Record · LEDGER

TransNet: Transferable Neural Networks for Partial Differential Equations

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.11701.

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

pith.paper-citation-record.v1
2301.11701 v1

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:05:09.813319Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T20:36:35.277241Z

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 5c08eaaf-109d-405b-863b-3f3db4deff7e · inbound

Deep Tangent Bundle (DTB) method: a Deep Neural Network approach to compute solutions of PDES cites this paper.

Deep Tangent Bundle (DTB) method: a Deep Neural Network approach to compute solutions of PDES TransNet: Transferable Neural Networks for Partial Differential Equations

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T13:05:09.813319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:05:09.813319Z digest=sha256:a6b5e4ec50843d5a609b2c97442e91cebdd8cfdd108d86d80ead04e58bf289aa

Observation 03204d14-65d6-4475-9b80-e77bd875e9e3 · inbound

Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method cites this paper.

Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method TransNet: Transferable Neural Networks for Partial Differential Equations

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-05-15T20:36:35.279191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T20:34:44.774525Z digest=sha256:891c6f606009722bdb2299fd92a160e33df35c2193152b9d802ad6bc9ca93135