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Paper Citation Record · LEDGER

One-shot learning for solution operators of partial differential equations

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2104.05512.

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

pith.paper-citation-record.v1
2104.05512 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:43.134940Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:49:58.384030Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 daed2437-53ba-49d7-b10c-d08dab62f7ef · inbound

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators cites this paper.

Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators One-shot learning for solution operators of partial differential equations

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-06T23:21:43.134940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:21:43.134940Z digest=sha256:d39dac9c44742911a470f19493b991df4f3a014fc7733ce928b2c18b5ea806a1

Observation 1602db4f-6914-46a1-8f3f-e0248e8b9ef3 · inbound

Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements cites this paper.

Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements One-shot learning for solution operators of partial differential equations

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:22:49.299650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-18T19:17:13.031769Z digest=sha256:04e7776f80e245825c6bf2f4fe1820c0522d9070c26ae68be43f5322bde38802

Observation 231e116d-19d2-494c-b4f6-5806a804b859 · inbound

PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations cites this paper.

PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations One-shot learning for solution operators of partial differential equations

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:15:51.738566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T18:38:35.104099Z digest=sha256:5bfd1d6bd2df09a78ecfd4d0ce8075e4dd453033e8ef47c2e10118000c09efc7

Observation 517bbd3f-5b31-42b6-b3e2-f8a8b6028501 · inbound

Towards a Foundation Model for the Martian Atmosphere cites this paper.

Towards a Foundation Model for the Martian Atmosphere One-shot learning for solution operators of partial differential equations

Reference 170

Resolution
verified exact
arxiv_id, observed 2026-06-30T19:05:00.825269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T19:01:31.373340Z digest=sha256:d481ca9538250be9d091d7b1a92e0e6d1956ec111a15b7762c581e41319796f1

Observation 263e2383-8312-4fc8-b4c5-6c6d64b1041e · inbound

A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients cites this paper.

A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients One-shot learning for solution operators of partial differential equations

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:49:58.385656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-06-26T00:10:26.127062Z digest=sha256:558804ae61dd247add0e4795f8de55ef901851da340d079f8dd948e3b92b6fef