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Paper Citation Record · LEDGER

Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2210.01074.

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

pith.paper-citation-record.v1
2210.01074 v1

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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T20:27:11.655674Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

3
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3494f3be-bb4f-4ea1-9642-8e786c10b7c1 · 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 Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-24T09:39:17.225740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-24T09:36:59.102360Z digest=sha256:710c78b0ebc347aa4d76e49921e8f82de2f31acf5dc0e4d3b50ff183cc92c060

Observation 2f335edd-e935-4019-aeba-a7e2b6d4e461 · inbound

FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning cites this paper.

FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-18T16:06:35.144940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T16:03:36.833056Z digest=sha256:1bda8fb48d3a66daeafb03e1cb0f658ab0166915cab1acac766512f29d7f27cd

Observation 8ab733ef-c644-46f3-8a00-9e77d7ae596c · inbound

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions cites this paper.

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-20T07:08:06.386321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T07:06:53.159609Z digest=sha256:ad94bd7bb64567e9860e5f01c73ab4ae185a83b7d1a6aaa35002a6912126a773

Observation 192f6e9c-b3b2-47ae-8745-72f5489b5278 · inbound

IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients cites this paper.

IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-06-29T23:54:03.190478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T23:53:30.631124Z digest=sha256:3f9197c41d173d0401d11d808c1355c624bfaa224655143692edb55a2939569b

Observation 3e4cc35c-3702-4e9f-805d-8fa3ffbef748 · inbound

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields cites this paper.

Inverse Low-Dimensional Manifold Reconstruction Framework for Spatiotemporal Reconstruction of Compressible Physical Fields Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-09T20:36:31.520397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-09T20:27:11.655674Z digest=sha256:8db8b712384e1538373a52d6d110cf84d5127e9375c97d5013b8a44aa301ffe5