Paper Citation Record · LEDGER

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2504.08730.

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

pith.paper-citation-record.v1
2504.08730 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T21:02:01.705689Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:09.574040Z

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 e4cb0419-bb81-4f94-b0d6-43ed7fdccc8a · inbound

Tucker Tensor Train Taylor Series cites this paper.

Tucker Tensor Train Taylor Series Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-13T21:02:01.705689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bc88e91-80f4-4706-a332-99ba3c6b9856 · inbound

Universal Approximation of Nonlinear Operators and Their Derivatives cites this paper.

Universal Approximation of Nonlinear Operators and Their Derivatives Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:52:40.251563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 719ea667-ca82-4909-95ff-148b6e2a9f13 · inbound

Universal Approximation of Nonlinear Operators and Their Derivatives cites this paper.

Universal Approximation of Nonlinear Operators and Their Derivatives Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-06-30T20:55:04.262928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3af02364-ecad-43a8-a9bc-81a8d8ce747b · inbound

Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Control cites this paper.

Derivative-Informed Operator Learning for Finance: On-the-Fly Greeks, Surfaces, Hedging, and Control Dimension reduction for derivative-informed operator learning: An analysis of approximation errors

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T16:17:09.575453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.