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Paper Citation Record · LEDGER

Neural Inverse Operators for Solving PDE Inverse Problems

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2301.11167.

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

pith.paper-citation-record.v1
2301.11167 v2

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measured 0 of 0 reference resolution

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measured 15 of 15 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:02:03.410551Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:27:27.403103Z

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Outbound references

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Pith citing papers

Observation 7f99fa73-db34-4b92-9d18-fdb267fc8b6b · inbound

A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results cites this paper.

A DeepONet for inverting the Neumann-to-Dirichlet Operator in Electrical Impedance Tomography: An approximation theoretic perspective and numerical results Neural Inverse Operators for Solving PDE Inverse Problems

Reference 64

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verified exact
arxiv_id, observed 2026-05-23T22:48:32.830724Z

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.

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Observation 59232128-7f14-4bb8-b2bb-b5154790f94e · inbound

Generative Prior-Guided Neural Interface Reconstruction for 3D Electrical Impedance Tomography cites this paper.

Generative Prior-Guided Neural Interface Reconstruction for 3D Electrical Impedance Tomography Neural Inverse Operators for Solving PDE Inverse Problems

Reference 33

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arxiv_id, observed 2026-05-22T02:40:56.899014Z

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.

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Observation cf77c6d6-49a0-48af-a34f-01db6000c54b · inbound

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery cites this paper.

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery Neural Inverse Operators for Solving PDE Inverse Problems

Reference 16

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Unavailable: canonical work link unavailable.

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Observation eea14b45-b327-4f72-b1fe-34913bf3f338 · inbound

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach cites this paper.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Neural Inverse Operators for Solving PDE Inverse Problems

Reference 184

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no resolver link, observed 2026-08-07T10:54:50.301472Z

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Unavailable: canonical work link unavailable.

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Observation 018afcb0-1067-4159-aa65-2c9557a2a6b4 · inbound

An Attention-based Spatio-Temporal Neural Operator for Evolving Physics cites this paper.

An Attention-based Spatio-Temporal Neural Operator for Evolving Physics Neural Inverse Operators for Solving PDE Inverse Problems

Reference 27

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no resolver link, observed 2026-08-07T04:18:14.123046Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:18:14.123046Z digest=sha256:07c5b018b83db1b29ee570d038fae32537a3ee9659121b601da13e6701fc8864

Observation c1530582-0ccb-4448-bfe8-d3a7fa1a337d · inbound

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

VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models Neural Inverse Operators for Solving PDE Inverse Problems

Reference 52

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no resolver link, observed 2026-08-07T00:31:13.889238Z

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Unavailable: canonical work link unavailable.

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Observation 1b81aa5c-d7b8-452b-aba9-e223b01a0c9a · 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 Neural Inverse Operators for Solving PDE Inverse Problems

Reference 54

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arxiv_id, observed 2026-05-19T04:12:02.561520Z

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.

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Observation 440fdadd-bdf4-4df2-a493-3394dfc34e45 · inbound

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

Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements Neural Inverse Operators for Solving PDE Inverse Problems

Reference 57

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arxiv_id, observed 2026-05-18T19:22:49.317043Z

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.

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Observation f56f5369-b64f-4bc7-b3ce-56c9af6d8f78 · inbound

Physics-informed sensor coverage through structure preserving machine learning cites this paper.

Physics-informed sensor coverage through structure preserving machine learning Neural Inverse Operators for Solving PDE Inverse Problems

Reference 51

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Observation 344ef5ec-08aa-4501-aa05-9ba54aaf54d3 · inbound

Neural Operator Representation of Granular Micromechanics-based Failure Envelope cites this paper.

Neural Operator Representation of Granular Micromechanics-based Failure Envelope Neural Inverse Operators for Solving PDE Inverse Problems

Reference 65

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arxiv_id, observed 2026-05-11T13:21:14.515817Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8e82b25a-3370-4f2f-8468-b7779f6f9c43 · 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 Neural Inverse Operators for Solving PDE Inverse Problems

Reference 33

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verified exact
arxiv_id, observed 2026-07-02T23:27:27.404708Z

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.

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Observation fcfad2fa-bdc2-46f7-a688-d21f3843109c · inbound

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime cites this paper.

Operator learning for the 2D incompressible Navier-Stokes equations: a conformal prediction approach in the data-scarce regime Neural Inverse Operators for Solving PDE Inverse Problems

Reference 28

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verified exact
arxiv_id, observed 2026-07-02T22:47:25.769380Z

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.

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Observation 62f4f337-7af3-4453-a5b1-fb5ed8fd9737 · inbound

Recovering Sharp Conductivity Features in the Finite-Data Calder\'on Problem with Physics-Informed Neural Networks cites this paper.

Recovering Sharp Conductivity Features in the Finite-Data Calder\'on Problem with Physics-Informed Neural Networks Neural Inverse Operators for Solving PDE Inverse Problems

Reference 14

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verified exact
arxiv_id, observed 2026-06-29T19:23:54.396187Z

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.

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Observation 73c8303b-081a-4826-9f64-8ec8e45d4c53 · inbound

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

GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems Neural Inverse Operators for Solving PDE Inverse Problems

Reference 32

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arxiv_id, observed 2026-07-02T15:37:05.897572Z

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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:f35405e41bf3a3ac0ff4d4f1f6d997a1ffcb47a4fa28921f3a5c624e6cb71acb

Observation 7b42f8e2-7fae-420d-9864-3d7f09af0b9a · inbound

Benchmarking Multi-fidelity Neural Operators on Complex PDE Problems with Non-trivial Fidelity Differences cites this paper.

Benchmarking Multi-fidelity Neural Operators on Complex PDE Problems with Non-trivial Fidelity Differences Neural Inverse Operators for Solving PDE Inverse Problems

Reference 9

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no resolver link, observed 2026-08-06T18:32:33.451506Z

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Unavailable: canonical work link unavailable.

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