Paper Citation Record · LEDGER

Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2312.14276.

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

pith.paper-citation-record.v1
2312.14276 v3

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 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:40:45.491431Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:14:57.105861Z

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 841895b8-27ee-4c66-b85b-1017b7f5bcfa · inbound

KAN: Kolmogorov-Arnold Networks cites this paper.

KAN: Kolmogorov-Arnold Networks Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:42:05.168312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c81405ec-f4bb-4928-84e8-e0061d767c00 · inbound

Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants cites this paper.

Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T04:40:45.491431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baad7947-eed4-4a03-b002-1d2a1029df39 · inbound

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions

Reference 128

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:31:14.043023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfabd71f-e33a-4a77-8244-d2f75ca36baa · inbound

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations cites this paper.

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:57.107520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.