Paper Citation Record · LEDGER

TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.10771.

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

pith.paper-citation-record.v1
2404.10771 v2

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 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:41:24.833094Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T09:23:37.349673Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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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 9e028d99-ff2c-443b-8efc-2ef695bd85ea · inbound

High precision PINNs in unbounded domains: application to singularity formulation in PDEs cites this paper.

High precision PINNs in unbounded domains: application to singularity formulation in PDEs TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T18:41:24.833094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d61447b-51fb-4aa5-bdf4-50cd1f900179 · inbound

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs cites this paper.

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:01:48.763725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d840951-95f7-4966-8c99-fff55d42c90f · inbound

Error analysis for learning the time-stepping operator of evolutionary PDEs cites this paper.

Error analysis for learning the time-stepping operator of evolutionary PDEs TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T10:24:35.673150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93a4e9cb-d1dd-4880-b019-c9dd85dbbdc7 · inbound

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches cites this paper.

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:23:37.351406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.