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Paper Citation Record · LEDGER

Neural operator surrogate models of plasma edge simulations: feasibility and data efficiency

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.17386.

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

pith.paper-citation-record.v1
2502.17386 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:38.159079Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T00:43:25.463521Z

Reference resolution

0 of 0 outbound references displayed

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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 cda3e721-a224-46bb-8608-76fe3ddc0903 · inbound

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning cites this paper.

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning Neural operator surrogate models of plasma edge simulations: feasibility and data efficiency

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:38.159079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:19:38.159079Z digest=sha256:0f53908aad2646a2a07f6065a7a50831a7a37425ba210bb061755550cdee4471

Observation a960d682-762b-4409-b28b-fb99c386841b · inbound

Challenges and opportunities for AI to help deliver fusion energy cites this paper.

Challenges and opportunities for AI to help deliver fusion energy Neural operator surrogate models of plasma edge simulations: feasibility and data efficiency

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-15T00:43:25.464914Z

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=2026-05-15T00:39:46.244035Z digest=sha256:a7485233ae32c0b17f09837214db6b68459ff96499595daadcf92690d463003c