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Paper Citation Record · LEDGER

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs

As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2607.23466.

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pith.paper-citation-record.v1
2607.23466 v1

Coverage vector

measured 23 of 23 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-30T21:35:27.316706Z

measured 23 of 23 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

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23 of 23 outbound references displayed

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Outbound references

Observation 38b1c34d-2d96-415f-994e-a6e2ec5a66ee · outbound

This paper cites HyperFNO: Improving the general- ization behavior of Fourier neural operators.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs HyperFNO: Improving the general- ization behavior of Fourier neural operators

Reference 1

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Observation 22e7e15e-0dad-431c-b5b0-bf8abd4a674a · outbound

This paper cites Sharing main- tains parameter efficiency, while separating allows each variable to evolve differently in latent space.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Sharing main- tains parameter efficiency, while separating allows each variable to evolve differently in latent space

Reference 2

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Observation 6fa27cef-2383-4fe9-ba41-82ddf40f3535 · outbound

This paper cites PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations

Reference 3

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Observation 15e66cd7-2195-43cf-9e8f-edf66b3a70c5 · outbound

This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 7

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Observation a13bd73d-05cf-4453-8c31-7329ca1543ac · outbound

This paper cites While the proposedhpFNO x exhibits a mild degradation at the largestν m, it still achieves the lowest overall errors (nRMSE≈0.015–0.018).

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs While the proposedhpFNO x exhibits a mild degradation at the largestν m, it still achieves the lowest overall errors (nRMSE≈0.015–0.018)

Reference 8

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Observation 11f9e412-e81f-44fc-8e39-d167c1507ed3 · outbound

This paper cites Detecting strange attractors in turbulence.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Detecting strange attractors in turbulence

Reference 9

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Observation 240d1e86-04ea-41a7-9489-fa0e9bbdfe7d · outbound

This paper cites Each variable is treated as an independent function,a(x) = (aα(x), aβ(x)), and discretized separately asa α,a β ∈R m.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Each variable is treated as an independent function,a(x) = (aα(x), aβ(x)), and discretized separately asa α,a β ∈R m

Reference 10

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Observation 3a86e1c6-0347-4b17-817f-7fc33abc7c1a · outbound

This paper cites an unresolved cited work.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Unresolved cited work

Reference 11

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Observation c696c3ab-ee2c-41c2-a205-d143ba95ce7e · outbound

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Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Unresolved cited work

Reference 12

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Observation e13d11f9-48dd-493c-b13d-84e30831974e · outbound

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Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Unresolved cited work

Reference 14

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Observation 7b7c8d65-ea6f-4bed-8cb4-9f98d07ac9e7 · outbound

This paper cites an unresolved cited work.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Unresolved cited work

Reference 15

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Observation 83065754-d57c-4560-bace-726bf29a48da · outbound

This paper cites an unresolved cited work.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Unresolved cited work

Reference 16

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Observation 48f7e377-37b2-4e8d-b039-2b97363ea88c · outbound

This paper cites All Figures present the two spatio-temporal fields, the electron densityn e(x, t)and the electric potentialϕ(x, t), in thet−xplane, in a heatmap format.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs All Figures present the two spatio-temporal fields, the electron densityn e(x, t)and the electric potentialϕ(x, t), in thet−xplane, in a heatmap format

Reference 17

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Observation eda6230b-c835-403d-948e-93e0ee3ceae7 · outbound

This paper cites Table 8 reports the performance of all considered models on ID test samples and OOD test samples.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Table 8 reports the performance of all considered models on ID test samples and OOD test samples

Reference 20

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Observation 9497c580-4de5-4c16-ab96-085641ecd6e5 · outbound

This paper cites Table 11: Performance comparisons between the proposed methods and the baseline methods.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Table 11: Performance comparisons between the proposed methods and the baseline methods

Reference 23

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Observation 19cdfb43-1ab3-4ddf-b10f-d9bcd0ee22ee · outbound

This paper cites In the experiments described in the main body, we consider the Taylor variant; in the Appendix, we provide the results of both variants.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs In the experiments described in the main body, we consider the Taylor variant; in the Appendix, we provide the results of both variants

Reference 128

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Observation b6d41dca-4d0b-49d8-8fbd-575d5a7dd050 · outbound

This paper cites Deeper or narrower configurations, such as a 3-layer network (width 256, nRMSE = 0.0200) or 3- layer network (width 32, nRMSE = 0.0230), show a mild degradation in performance.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Deeper or narrower configurations, such as a 3-layer network (width 256, nRMSE = 0.0200) or 3- layer network (width 32, nRMSE = 0.0230), show a mild degradation in performance

Reference 256

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Observation feab0e22-939f-4319-ac83-f8bc24272238 · outbound

This paper cites an unresolved cited work.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Unresolved cited work

Reference 2014

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Observation 75dfa0a0-a793-4bcb-b34a-aa7cdc3068bd · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Adam: A Method for Stochastic Optimization

Reference 2015

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Observation c7472d54-2583-4ef1-bf3a-53e1d88703bf · outbound

This paper cites Kingma and Jimmy Ba.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Kingma and Jimmy Ba

Reference 2021

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This paper cites URLhttps: //openreview.net/forum?id=j3oQF9coJd.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs URLhttps: //openreview.net/forum?id=j3oQF9coJd

Reference 2023

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Observation ee2a6f51-8e01-4f2b-99ac-3d9f66ffeb9f · outbound

This paper cites Projection-based model reduction for reacting flows.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs Projection-based model reduction for reacting flows

Reference 2024

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Observation 0afcb281-99c0-4764-9aaa-543f2a31f2f2 · outbound

This paper cites The Sol supercomputer at Arizona State University.

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs The Sol supercomputer at Arizona State University

Reference 2025

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