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Paper Citation Record · LEDGER

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2207.05209.

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

pith.paper-citation-record.v1
2207.05209 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:24:44.060145Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:17:57.352495Z

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 da57935c-8a4b-4d17-9e47-d58b4a54d89c · 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 Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-24T09:39:17.188322Z

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:b660ebe1826de23ad1d76cfcd771f51c213cc8189710cb9971dcb5d587b59f9c

Observation 2bcdb51a-e919-49a6-8e31-f08378d7bc76 · inbound

Latent Mamba Operator for Partial Differential Equations cites this paper.

Latent Mamba Operator for Partial Differential Equations Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T14:24:44.060145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:24:44.060145Z digest=sha256:37fbe18068b53a51307d9b3aff3333ab406f53a78f58879533d00233ff41bd85

Observation be9ba6c2-0d18-4d33-a054-3283d60e536c · inbound

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids cites this paper.

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T06:05:53.372875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:05:53.372875Z digest=sha256:11c58000384c33ea371f9589ebb688da33ab631878a2bb9189f811bbca1bcb98

Observation 615e819e-56c2-4aeb-acb7-b73d007c3401 · inbound

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling cites this paper.

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 22

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T10:22:14.701807Z

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=2026-05-19T10:17:34.190090Z digest=sha256:a79d6590d3dd11fb46f8dcef07f2809eb13c8d6d51856095a7ba1798694a2cfb

Observation fd78b97f-5764-4675-a6f7-af4e67301674 · inbound

GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations cites this paper.

GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T00:29:33.966479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:29:33.966479Z digest=sha256:e1f408cc02cb1b39f1de8320cc20217e0cc68279baacb8d170d6e56bb3cf2cd2

Observation 85bdbd3a-029b-49db-b563-9dae0998d8e8 · inbound

Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting cites this paper.

Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T15:31:59.723616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:31:59.723616Z digest=sha256:7996f1d4754b49dd316c982611d4ed84496e7c4fa75fc1f78c6ef2d6df02f231

Observation 13946365-2cc7-403f-be3f-8cbe01f87d11 · inbound

Deep Gaussian Processes for Functional Maps cites this paper.

Deep Gaussian Processes for Functional Maps Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-18T04:05:50.876904Z

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=2026-05-18T04:03:53.495145Z digest=sha256:146a8e70ca2883ef0d8a97043fe62b2d87005efa65245fecf9f1ed6170b4da5a

Observation d4f1512e-f798-4740-be32-a0f810850861 · inbound

HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems cites this paper.

HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T10:17:57.353865Z

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=2026-06-27T10:11:45.429648Z digest=sha256:94582222a059048804a3501cd5aef507136ed92766d5a8bb421f494d9e2c33dc