Paper Citation Record · LEDGER

Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.10680.

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

pith.paper-citation-record.v1
2402.10680 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:30:10.003701Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T09:34:05.810507Z

Reference resolution

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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 a4abfee3-29d2-4302-93ae-4d47455954fa · inbound

Expansive Natural Neural Gradient Flows for Energy Minimization cites this paper.

Expansive Natural Neural Gradient Flows for Energy Minimization Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T16:30:10.003701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c6c5857-ad09-44a2-8c90-c3551dec845b · inbound

On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime cites this paper.

On the Convergence Behavior of Preconditioned Gradient Descent Toward the Rich Learning Regime Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:23:09.892775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 75125279-8292-4c63-aa10-bf3e233e5da3 · inbound

Neural-network methods for two-dimensional finite-source reflector design cites this paper.

Neural-network methods for two-dimensional finite-source reflector design Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-21T09:34:05.812779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c9bd5764-2eb3-4145-8cbc-cc747f015121 · inbound

Natural gradient descent with momentum cites this paper.

Natural gradient descent with momentum Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:25:20.180420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec039b01-c9ed-44c1-a279-423e4cdcba9a · inbound

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

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

Reference 182

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:23:37.345245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1bc946e1-0e5c-449e-9bd5-5daf4353a5f4 · inbound

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers cites this paper.

Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers Gauss-Newton Natural Gradient Descent for Physics-Informed Computational Fluid Dynamics

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T06:09:01.657091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.