Paper Citation Record · LEDGER

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling

As of 19 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 1 inbound Pith citation observation for arXiv:2502.06250.

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

pith.paper-citation-record.v1
2502.06250 v3

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:20:51.082011Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:57:32.202231Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T20:57:32.259547Z

Reference resolution

69 of 69 outbound references displayed

verified exact4
verified fuzzy16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6664a8bb-e176-4221-999a-2ea92a4f3941 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.166608Z

Source-reported events for the cited work

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Observation fbcbbc06-a1fd-4cd4-bba4-99d1fecfeacf · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.157410Z

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Observation fd94e7fc-8439-44b0-b6bd-2d0f269161f6 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.148794Z

Source-reported events for the cited work

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Observation f12241dd-b9a0-4d51-a701-15b2e754c975 · outbound

This paper cites Meakin, Models for material failure and deformation, Science 252 (5003) (1991) 226–234.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Meakin, Models for material failure and deformation, Science 252 (5003) (1991) 226–234

Reference 4

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Source-reported events for the cited work

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Observation 1dc601d6-46b7-41bf-a0db-a99d9e15626e · outbound

This paper cites Adler, D.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Adler, D

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:52.131393Z

Source-reported events for the cited work

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Observation f9e744da-d7f5-4ae8-ad18-330a0f7c47f0 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.122383Z

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Observation e7014c64-454b-4635-b66f-da2b9f4afc2f · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 7

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Observation ebc07ef8-a460-4868-a5b6-589f237bb68b · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.108574Z

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Observation 7aff187e-8497-460a-907e-1f189e2f2423 · outbound

This paper cites Gholizadeh, A review of non-destructive testing methods of composite materials, Procedia structural integrity 1 (2016) 50–57.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Gholizadeh, A review of non-destructive testing methods of composite materials, Procedia structural integrity 1 (2016) 50–57

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:52.099775Z

Source-reported events for the cited work

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Observation 524abf9e-06e1-4cee-b0b4-c2d9b760e610 · outbound

This paper cites Zang, P.-S.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Zang, P.-S

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:52.090622Z

Source-reported events for the cited work

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Observation 2368a4e6-31ee-4b6a-bb26-8ba30b166928 · outbound

This paper cites Raissi, P.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Raissi, P

Reference 11

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This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.076651Z

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Observation 50adcd42-d679-4993-93fe-a314505352cd · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.067982Z

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Observation 1e5b61df-3419-440e-ab8e-cebdcd655928 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation 48e87b68-6c26-424d-9b82-e03264e088ea · outbound

This paper cites Yu, et al., The deep ritz method: a deep learning-based numerical algorithm for solving variational problems, Communications in Mathematics and Statistics 6 (1) (2018) 1–12.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yu, et al., The deep ritz method: a deep learning-based numerical algorithm for solving variational problems, Communications in Mathematics and Statistics 6 (1) (2018) 1–12

Reference 15

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Observation 6ea694de-5558-4bdf-8cd9-81b0aba25dac · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.045814Z

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Observation 00af87ca-2d58-4fde-86e1-6bc263b65198 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:52.036908Z

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Observation e6ccefbe-5a0e-4b08-90be-733a5b4338fe · outbound

This paper cites Variational Physics-Informed Neural Networks For Solving Partial Differential Equations.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Reference 18

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This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 19

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This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 1fc33bd3-3efc-49eb-a315-81d5f7bbe161 · outbound

This paper cites Sirignano, K.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Sirignano, K

Reference 21

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This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 22

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Observation 303b84b7-4110-438f-b710-4a54f1312d38 · outbound

This paper cites On Robustness of Neural Ordinary Differential Equations.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling On Robustness of Neural Ordinary Differential Equations

Reference 23

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Observation 128725b0-3fd0-4c57-99c7-45d5c79587f9 · outbound

This paper cites Mowlavi, S.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Mowlavi, S

Reference 24

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Observation 7ff1d0bd-5828-4e23-b550-4965237d2e6c · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 25

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This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 26

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Observation de59b917-6d7d-4bcc-a6a6-2cc412f3e36f · outbound

This paper cites Kaltenbach, P.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Kaltenbach, P

Reference 27

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Observation 350b21b8-3fbf-4721-a82e-2e6667c77d86 · outbound

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

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 28

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Observation e8dfad3d-0a0d-4ade-96b9-a5497fb95716 · outbound

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

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Fourier Neural Operator for Parametric Partial Differential Equations

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:50.935916Z

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This paper cites Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

Reference 30

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DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 31

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Observation 3795f521-05ec-4a7a-8bc8-69c08d69e145 · outbound

This paper cites Tripura, S.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Tripura, S

Reference 32

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Observation c8205df7-86d5-4c41-9a6e-2039d3f1bfd6 · outbound

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DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 33

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DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 34

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Observation b1c4a0ac-83bf-4c55-9622-aba983987f32 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 35

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This paper cites Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media

Reference 36

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Observation 8989c2b9-22d8-4efb-9ae1-f23f8fbf0727 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 37

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This paper cites Physics-Informed Deep Neural Operator Networks.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Physics-Informed Deep Neural Operator Networks

Reference 38

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Observation 192db911-4db3-4a96-88db-3dfb17b39bfe · outbound

This paper cites Goswami, M.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Goswami, M

Reference 39

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This paper cites Navaneeth, T.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Navaneeth, T

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.944929Z

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Observation cbce02ed-603e-4c16-a367-9232d675ef99 · outbound

This paper cites Gupta, X.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Gupta, X

Reference 41

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Observation d3d97899-9c74-458f-b26d-48c978018174 · outbound

This paper cites Zhong, H.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Zhong, H

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.926028Z

Source-reported events for the cited work

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Observation 69276d4c-0cfb-43d7-b73f-ecd934912817 · outbound

This paper cites Zhong, H.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Zhong, H

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.916277Z

Source-reported events for the cited work

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Observation 49aa8539-7b23-4a8b-bd42-7bbbf3bb3500 · outbound

This paper cites Kashefi, T.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Kashefi, T

Reference 44

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DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 45

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This paper cites Vadeboncoeur, I.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Vadeboncoeur, I

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.893561Z

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Observation 65d86268-cf02-4526-8d4d-3db89ebbbb24 · outbound

This paper cites Vadeboncoeur, O.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Vadeboncoeur, O

Reference 47

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Source-reported events for the cited work

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Observation 436a67c3-4098-4a32-8fcb-d13c63d2d956 · outbound

This paper cites Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators

Reference 48

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DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Rixner, P.-S

Reference 49

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Observation e5ade564-75ee-4fe8-aef9-bcd152954ebd · outbound

This paper cites Kaltenbach, P.-S.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Kaltenbach, P.-S

Reference 50

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Source-reported events for the cited work

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DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:51.883626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c467b4d-aab8-4673-92bf-d67e6a5948be · outbound

This paper cites ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-08T16:20:51.365178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 01ac9f47-10eb-4b23-9758-270b8234678e · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:51.874148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e66d44e6-9ffe-4074-90d6-35eae4705c43 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:51.030625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8504a325-755f-446c-8373-b48c9f9daf0d · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:51.859250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cd87a6ff-22e7-4840-a5a5-c7bdb4eeed7f · outbound

This paper cites Bayesian neural networks for weak solution of PDEs with uncertainty quantification.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Bayesian neural networks for weak solution of PDEs with uncertainty quantification

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:51.037441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06d5740d-005a-4b39-971d-0909a84b9486 · outbound

This paper cites Vadeboncoeur, ¨O.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Vadeboncoeur, ¨O

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.848982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e582f7d3-346b-4d86-b470-b77ba77a8f5a · outbound

This paper cites Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:51.044668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f49b37eb-635a-449b-b4db-b202e63ce672 · outbound

This paper cites Alberts, I.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Alberts, I

Reference 59

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T16:20:51.328081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 84dd471d-f199-44e2-b7c2-6bc64a730dd6 · outbound

This paper cites Ganguly, S.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Ganguly, S

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.838287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4cd04dc4-deee-4d6c-8838-3b9d409f4884 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 61

Resolution: verified exact; doi, observed 2026-08-08T16:20:51.124344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 688b22ec-fba1-4990-8dbe-08608453c399 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:51.059335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0ebdd51-292c-4a71-bc79-52f2ec99d16e · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:51.821528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 199f4837-d54c-47b5-bb5e-89a9ab72567d · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:51.811496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae601f07-667d-408e-9622-d0c0dd7d6064 · outbound

This paper cites Bourke, Cross correlation, Cross Correlation”, Auto Correlation—2D Pattern Identification 596 (1996).

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Bourke, Cross correlation, Cross Correlation”, Auto Correlation—2D Pattern Identification 596 (1996)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.801477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2845ce47-8a41-45e2-9143-93453574d909 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-08T16:20:51.792009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bb954bf-37ef-4836-8d0f-671c0f040a70 · outbound

This paper cites Bastek, D.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Bastek, D

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T16:20:51.075574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cffb9df7-edfd-4bf3-9a03-d1217f3fb029 · outbound

This paper cites an unresolved cited work.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Unresolved cited work

Reference 68

Resolution: metadata mismatch; raw_fallback, observed 2026-08-08T16:20:51.258304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7c86622a-8aa9-4085-841d-15ae7274266b · outbound

This paper cites particles.

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling particles

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:20:51.782844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 6b00d0e4-8e21-4bf2-810c-33a90d4b9b68 · inbound

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data cites this paper.

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:57:32.263767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.