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Paper Citation Record · LEDGER

A Mathematical Guide to Operator Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2312.14688.

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

pith.paper-citation-record.v1
2312.14688 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:44.069257Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T02:59:25.642553Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8442d086-e108-4e46-9fcc-4e866c0affd3 · inbound

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning cites this paper.

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning A Mathematical Guide to Operator Learning

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-23T19:33:22.114348Z

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.

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Observation 8acbcd3f-1dde-4d6d-840c-cc04984f5785 · inbound

Graph-Based Operator Learning from Limited Data on Irregular Domains cites this paper.

Graph-Based Operator Learning from Limited Data on Irregular Domains A Mathematical Guide to Operator Learning

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T14:27:44.069257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:27:44.069257Z digest=sha256:022448c62f2d62eb5b678224dcc3dc351d36af6c280580edadf6447e360e9922

Observation 67e222c7-ad40-4e5f-b3df-2ef45cbbdbb6 · 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 A Mathematical Guide to Operator Learning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T04:19:37.504618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 570f73eb-9b3e-4883-b6c7-bb59819b0fbe · inbound

Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory cites this paper.

Matrix-free Neural Preconditioner for the Dirac Operator in Lattice Gauge Theory A Mathematical Guide to Operator Learning

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-04T17:57:20.511261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:57:20.511261Z digest=sha256:c9a18642a157accfa65dc5b503fc3249de60215bcc59ea18727aea40779757ac

Observation 664e1b58-f793-4640-999f-318a216bb0da · inbound

Man, Machine, and Mathematics cites this paper.

Man, Machine, and Mathematics A Mathematical Guide to Operator Learning

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:51:27.747750Z

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=2026-05-07T09:04:54.618705Z digest=sha256:8fd53930c3410b01628571f845538ecf38179ae00221ece2f91dbeb318a7261b

Observation 3f64bc41-2036-43d1-8677-9811cbdc623f · inbound

Is Zero-Shot Super-Resolution Possible in Operator Learning? cites this paper.

Is Zero-Shot Super-Resolution Possible in Operator Learning? A Mathematical Guide to Operator Learning

Reference 48

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T19:52:35.016227Z

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=arxiv_source observed=2026-06-28T19:52:06.416882Z digest=sha256:88764b7c8007e5090e89ad35517bf5850a0ebcceb3a3342b3d85cd6c8d77bd03

Observation 9be998cf-ced8-4db8-bae5-bb01a7d87f9c · inbound

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime cites this paper.

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime A Mathematical Guide to Operator Learning

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-02T22:47:25.754328Z

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=2026-06-27T18:39:56.578189Z digest=sha256:25b0dfe53bd505384b4c71f74b212279d1b79c5ffd13c70a1489bdc50beae862

Observation c9459d08-934b-4412-a22d-1320273601f0 · inbound

Patnaik-Pearson intrinsic dimension for internal representations of neural networks cites this paper.

Patnaik-Pearson intrinsic dimension for internal representations of neural networks A Mathematical Guide to Operator Learning

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T02:59:25.645024Z

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=2026-06-26T18:42:23.506693Z digest=sha256:29d4fa84d704c8cee7a17c0f6c2e839834100e7a85760d2bd392a86999636cb6

Observation 81ef8b1d-8d9b-4b24-9f38-bc9bec5ab133 · inbound

Patnaik-Pearson intrinsic dimension for internal representations of neural networks cites this paper.

Patnaik-Pearson intrinsic dimension for internal representations of neural networks A Mathematical Guide to Operator Learning

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T23:49:02.135302Z

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=2026-07-03T23:41:26.610335Z digest=sha256:531f845514b7d8d6675444607b490220a32192385462415acd3bfdeef182159d