Paper Citation Record · LEDGER

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel

As of 12 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 3 inbound Pith citation observations for arXiv:2412.05545.

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

pith.paper-citation-record.v1
2412.05545 v3

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:45:27.436771Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:34:06.756960Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T01:58:28.948681Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68c4dc6f-3e12-49b3-9730-24947928df34 · outbound

This paper cites Physics-informe d neural networks: A deep learning framework for solving forward and inverse problems involvin g nonlinear partial diﬀerential equations,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Physics-informe d neural networks: A deep learning framework for solving forward and inverse problems involvin g nonlinear partial diﬀerential equations,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.760377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0fff01ef-cfa6-4c77-a639-843d6b7bf9e6 · outbound

This paper cites The deep ritz method: a deep learning-based numerical algorithm for solving variational problems,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel The deep ritz method: a deep learning-based numerical algorithm for solving variational problems,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.748902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 28485265-6a53-47d0-8f67-79afbe51ad25 · outbound

This paper cites Optimal approximation rate of relu networks in terms of width and depth,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Optimal approximation rate of relu networks in terms of width and depth,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.737709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a292c38f-87b6-4167-a751-7727a2774611 · outbound

This paper cites Deep network approxim ation for smooth functions,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Deep network approxim ation for smooth functions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.726944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bebe7650-96ad-41cc-b155-869a02b2ce72 · outbound

This paper cites Error bounds for approximations with deep relu n etworks,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Error bounds for approximations with deep relu n etworks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.715963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55edba69-f047-48da-ac71-476289fd1887 · outbound

This paper cites Model reduction and neural networks for parametric pdes,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Model reduction and neural networks for parametric pdes,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.703838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87fc8b27-5c35-4335-b977-1929551aff46 · outbound

This paper cites Universal approximation to nonlinear oper ators by neural net- works with arbitrary activation functions and its application to dyna mical systems,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Universal approximation to nonlinear oper ators by neural net- works with arbitrary activation functions and its application to dyna mical systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.692751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation df3d8122-d50c-441d-b08a-dda5bf807454 · outbound

This paper cites Learnin g nonlinear operators via deeponet based on the universal approximation theorem of ope rators,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Learnin g nonlinear operators via deeponet based on the universal approximation theorem of ope rators,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.682337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 503ff57d-4a08-4443-8013-ed7292fbbfd1 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Fourier Neural Operator for Parametric Partial Differential Equations

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T20:45:27.366559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99b50ed5-f1e7-4aa6-a757-881a991834d3 · outbound

This paper cites Neural operator: Learning maps between function spac es with applications to pdes,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Neural operator: Learning maps between function spac es with applications to pdes,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.671829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 94b92119-70f7-4e7a-9ea9-74a94914be71 · outbound

This paper cites On universal appro ximation and error bounds for fourier neural operators,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel On universal appro ximation and error bounds for fourier neural operators,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.660639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3a82e6e2-0405-475d-8d2c-e44b0880caab · outbound

This paper cites Operator Learning: Algorithms and Analysis.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Operator Learning: Algorithms and Analysis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:45:27.379157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5693865a-b8da-4159-892f-f54ef4861d1e · outbound

This paper cites Error estimat es for deeponets: A deep learning framework in inﬁnite dimensions,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Error estimat es for deeponets: A deep learning framework in inﬁnite dimensions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.648738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 87e4c81e-bdcb-4d1f-9f69-2b99b2636f98 · outbound

This paper cites Deep nonparam etric estimation of operators between inﬁnite dimensional spaces,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Deep nonparam etric estimation of operators between inﬁnite dimensional spaces,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.635397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cffce490-2198-4db6-ade2-fe0ade5a0798 · outbound

This paper cites Optimizatio n for neural operator learn- ing: Wider networks are better.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Optimizatio n for neural operator learn- ing: Wider networks are better

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.623588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7317e5a-1349-47e5-8d80-38d72fe762a9 · outbound

This paper cites Improved architecture s and training algorithms for deep operator networks,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Improved architecture s and training algorithms for deep operator networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.611344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cb4508e1-a29a-4e87-84b0-a5b7faa69a47 · outbound

This paper cites Gradient Descent Provably Optimizes Over-parameterized Neural Networks.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Gradient Descent Provably Optimizes Over-parameterized Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:45:27.399136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54a1bebb-4cbe-4bb5-95b2-448b4c2291ff · outbound

This paper cites Gradient descent ﬁn ds global minima of deep neural networks,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Gradient descent ﬁn ds global minima of deep neural networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.599444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4f4a789c-660b-418e-8e5e-fce11f0f7a76 · outbound

This paper cites ReLU Deep Neural Networks and Linear Finite Elements.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel ReLU Deep Neural Networks and Linear Finite Elements

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T20:45:27.407243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5c15436-4554-4347-91cf-ce403efa1f7f · outbound

This paper cites Gradient descent ﬁnds the global op tima of two-layer physics- informed neural networks,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Gradient descent ﬁnds the global op tima of two-layer physics- informed neural networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.587746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a4b0ed0b-b84b-428f-8249-42d2fac127e1 · outbound

This paper cites Gin´ e and R.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Gin´ e and R

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.575621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8d424d37-1c94-46c7-bb17-dfb8f2df59da · outbound

This paper cites Moving beyond sub-g aussianity in high- dimensional statistics: Applications in covariance estimation and linea r regression,.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Moving beyond sub-g aussianity in high- dimensional statistics: Applications in covariance estimation and linea r regression,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.563176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3806d159-c3f1-40dd-a301-f6a9f991f98c · outbound

This paper cites an unresolved cited work.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:45:27.550333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e00c80c6-0a56-4377-b0bc-da05b3a50705 · outbound

This paper cites Proof of Lemma 1.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Proof of Lemma 1

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.538727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 024a2aef-1acd-41a8-b513-280cd69a219a · outbound

This paper cites Proof of Lemma 4.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Proof of Lemma 4

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.527061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3d8b0249-0cc7-4058-95d8-2d7b7019f229 · outbound

This paper cites Let X ∼ N (0,σ 2), then for any t> 0, 2 3 t σ <P (|X| ≤ t)< 4 5 t σ.

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel Let X ∼ N (0,σ 2), then for any t> 0, 2 3 t σ <P (|X| ≤ t)< 4 5 t σ

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:45:27.514436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation c2aa5254-e40d-4281-b302-0459b939cd8b · inbound

Optimal Convergence Rates for Neural Operators cites this paper.

Optimal Convergence Rates for Neural Operators Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T05:34:06.756960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc99a90d-d229-40b1-9170-66f73ee3354c · inbound

Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization cites this paper.

Optimization and generalization analysis for two-layer physics-informed neural networks without over-parametrization Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:23:02.200023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 938b4aa0-f45a-4b5c-a63f-694a7526b88c · inbound

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel cites this paper.

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:58:28.950387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.