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Paper Citation Record · LEDGER

Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2402.11722.

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

pith.paper-citation-record.v1
2402.11722 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 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:31:13.342602Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:27:27.393885Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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  • 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 0e502432-c9b1-4b9a-b84b-1d649d00db5e · inbound

VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models cites this paper.

VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T00:31:13.342602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:31:13.342602Z digest=sha256:fa13bc4fad24438164f69758d4258eee5e0a2fa3304c127417f6a52d8f1ada8d

Observation 1f8fefb2-51f2-480f-9642-4681113ea7ed · inbound

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

Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-18T19:22:49.265389Z

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=2026-05-18T19:17:13.031769Z digest=sha256:c702173777dbf4b9fd869154683f576626dfd1a101828c990d71381ee452d905

Observation e8e75aa8-405f-4337-a0a9-0e066a998c71 · inbound

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

FEDONet : Fourier-Embedded DeepONet for Spectrally Accurate Operator Learning Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-18T16:06:35.156329Z

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=2026-05-18T16:03:36.833056Z digest=sha256:c6d55dc9b58f1ec475b5b1b73b3e3517e359071c57a4c08d83f2a04d6609f591

Observation 5576e802-7829-44b3-a993-87f5195b790a · inbound

DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems cites this paper.

DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T09:31:05.676481Z

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=2026-05-10T15:58:12.225880Z digest=sha256:7fa62faef2302cb19a679d3a81f817c8f827367574990370c347a41fd5c2c2b5

Observation a5f704bb-79a8-48e7-ac02-024208124a77 · inbound

Multiscale Fourier Neural Operator for Inverse Wave Scattering in Highly Oscillatory Media cites this paper.

Multiscale Fourier Neural Operator for Inverse Wave Scattering in Highly Oscillatory Media Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:27:27.395902Z

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=2026-06-27T18:14:08.578216Z digest=sha256:04cfbc2ca6ec762b334227e7ba40d547fe369b6db83d90c4ec8dc7c2f77c4a39

Observation ef32899c-12eb-46f5-b7c6-f02ad3dd98bd · inbound

GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems cites this paper.

GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 33

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T15:37:05.894953Z

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=2026-07-02T15:33:33.670669Z digest=sha256:5e9ae2a80d87e0813a7a0004b89ef215a2532bfb684da9beae1c0770c7187038

Observation 061201dd-00ef-4281-8166-9c0be79a0e65 · inbound

Component-Level Inverse Design of Transmon Qubits Using Neural Networks cites this paper.

Component-Level Inverse Design of Transmon Qubits Using Neural Networks Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-01T09:27:12.891404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T09:27:12.891404Z digest=sha256:6b6c9f8a3d71c14b89e514105ad977047e11785f2e4fc4932e3f7a7b9f20ff6a

Observation f19f543b-f866-45e9-bacb-68e49ec148af · inbound

Component-Level Inverse Design of Transmon Qubits Using Neural Networks cites this paper.

Component-Level Inverse Design of Transmon Qubits Using Neural Networks Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-03T00:49:16.475169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T00:49:16.475169Z digest=sha256:249ed2a62908348b885eabaf0170ae573c54c4036c647265c3e9f12e13097db5