Paper Citation Record · LEDGER

Learning Neural PDE Solvers with Convergence Guarantees

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1906.01200.

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

pith.paper-citation-record.v1
1906.01200 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:41:09.122251Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T09:39:17.215467Z

Reference resolution

0 of 0 outbound references displayed

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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 43366af9-04b5-448c-976c-d5736d54fe63 · inbound

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations cites this paper.

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Learning Neural PDE Solvers with Convergence Guarantees

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-24T09:39:17.219607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cf72d1fc-5674-4e89-84c6-24f49a831cfd · inbound

Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators cites this paper.

Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators Learning Neural PDE Solvers with Convergence Guarantees

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:09.122251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea8f4f4d-78b9-489f-aa75-40ae0a13ffaa · inbound

Accurate and scalable deep Maxwell solvers using multilevel iterative methods cites this paper.

Accurate and scalable deep Maxwell solvers using multilevel iterative methods Learning Neural PDE Solvers with Convergence Guarantees

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T10:53:33.264101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00e1eb62-29cc-4808-b45b-cdd33fc90a1a · inbound

NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers cites this paper.

NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers Learning Neural PDE Solvers with Convergence Guarantees

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:35:36.300468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cec44e10-271e-4192-888d-14c584be837c · inbound

When can a neural operator replace a coarse solve? Architectural principles for two-level preconditioning cites this paper.

When can a neural operator replace a coarse solve? Architectural principles for two-level preconditioning Learning Neural PDE Solvers with Convergence Guarantees

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-20T02:02:58.461148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 83890b1e-13c4-433b-aa96-1325b0b3738e · inbound

NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust cites this paper.

NeuroForge: A Self-Correcting, Geometry-Native Neural CFD Engine with Calibrated Physics-Residual Trust Learning Neural PDE Solvers with Convergence Guarantees

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-14T12:34:40.550770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.