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Paper Citation Record · LEDGER

MgNO: Efficient Parameterization of Linear Operators via Multigrid

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.19809.

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

pith.paper-citation-record.v1
2310.19809 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:29:14.330942Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T23:54:03.210702Z

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 51844214-c8e7-4855-985e-e7c6642d1fa4 · inbound

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries cites this paper.

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-12T18:29:14.330942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5758ad22-d8ef-4e17-9da2-a299ee5f7811 · inbound

A deformation-based framework for learning solution mappings of PDEs defined on varying domains cites this paper.

A deformation-based framework for learning solution mappings of PDEs defined on varying domains MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T04:31:52.999441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:31:52.999441Z digest=sha256:7d8f9d4452720d3a9cecd4df7ffcb04ad608b72b0b096269df86159d00aa818d

Observation 3fe13b72-14d5-4226-877d-9d4692dc5f54 · inbound

Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries cites this paper.

Point Cloud Neural Operator for Parametric PDEs on Complex and Variable Geometries MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 119

Resolution
unresolved
no resolver link, observed 2026-08-10T15:12:46.421957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:12:46.421957Z digest=sha256:86600c57ef6dcd74b19f45ef3c75d416b8d9a2b7cdfae7310c8d76995b277ba9

Observation 57b755ed-0649-4fb4-afa4-0ae86a1e05f5 · inbound

OpenBreastUS: Benchmarking Neural Operators for Wave Imaging Using Breast Ultrasound Computed Tomography cites this paper.

OpenBreastUS: Benchmarking Neural Operators for Wave Imaging Using Breast Ultrasound Computed Tomography MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T15:47:31.929982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:47:31.929982Z digest=sha256:a57bb82fc60a59cc4e07d064d5742ef30852622d1ca5e6af1d17d18ab455c16a

Observation 3f0b3bfa-5fad-47a0-8bfd-9b2386b3220c · inbound

Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators cites this paper.

Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-19T04:12:02.564844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-19T04:07:42.061343Z digest=sha256:24d457d7336c05c930e0a3ba5b993e4c29efdb3359c0a29d53c2b4a55931c240

Observation 7ecdecf7-20d7-4484-b03e-7edaef06da3b · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 50

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T07:21:26.102589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-12T03:27:14.464987Z digest=sha256:653d3593c5b42b55f1a162a3481b15ad8c82e255c520e5878cf1b7a601b1a692

Observation 8ad93a95-9b1d-4051-a261-ff52674a34b2 · inbound

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting cites this paper.

PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 50

Resolution
metadata mismatch
arxiv_id, observed 2026-05-14T21:22:59.064531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-14T21:21:32.256476Z digest=sha256:44a23cfa2ddc72b94c0de38b4044ff08cdf708e7885067e16f20916420e499fd

Observation eb0f203d-c38c-4d0e-a665-d844d127b76a · inbound

Discovering Physical Directions in Weight Space: Composing Neural PDE Experts cites this paper.

Discovering Physical Directions in Weight Space: Composing Neural PDE Experts MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:08:29.764326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-15T02:03:50.528627Z digest=sha256:2ce8eb0d581dce81e2f6a0a74327c1ec297d0cdb99628e2298a6f24872ccf292

Observation 98e17488-88d7-4c4d-ac15-cae1776729a9 · 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 MgNO: Efficient Parameterization of Linear Operators via Multigrid

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-06-29T23:54:03.212163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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