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Paper Citation Record · LEDGER

Solving and Learning Nonlinear PDEs with Gaussian Processes

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2103.12959.

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

pith.paper-citation-record.v1
2103.12959 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:38:18.778014Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T12:14:11.755959Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • 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 2422990e-b674-4208-b2f3-2c7f719da540 · inbound

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications cites this paper.

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications Solving and Learning Nonlinear PDEs with Gaussian Processes

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-04T00:38:18.778014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:38:18.778014Z digest=sha256:0496f0c934df59c18b7fd8bd73d637c772afd4fdae39e68a7d76411df6957502

Observation 06922edd-4f28-4c7f-9442-ff30c0442d43 · inbound

Kernel-Based LMI Approaches to Solving the Hamilton-Jacobi-Bellman Equation and Nonlinear Optimal Control cites this paper.

Kernel-Based LMI Approaches to Solving the Hamilton-Jacobi-Bellman Equation and Nonlinear Optimal Control Solving and Learning Nonlinear PDEs with Gaussian Processes

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-21T12:14:11.757940Z

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.

source=pdf_text observed=2026-05-21T12:11:34.123475Z digest=sha256:0c950a240ca82cbae6148b674e1b3b6b2f2359b439a6fec959692c1a2594f345