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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 10 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations 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 69 of 69 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

69 of 69 outbound references displayed

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  • verified fuzzy16
  • unresolved46
  • parse uncertain0
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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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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

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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

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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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Observation 49d703cb-e51b-4b4c-9967-c01b35c8879f · 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 12

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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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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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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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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

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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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Observation 45176496-0cf6-4931-bf37-47dfa59dfab6 · 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 19

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Observation 7bc36cf3-92e0-44fe-b519-16f380289ba8 · 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 20

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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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Observation 315a63e4-8dd7-4a67-a641-d191aea4e5b5 · 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 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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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Observation 3639908e-8a32-4117-a4d3-be5d208d7ade · outbound

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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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 31

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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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Observation dea29ff1-8904-4d31-9fbc-d6f6650f3b9e · 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 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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source=pdf_text observed=2026-08-08T16:20:50.959628Z digest=sha256:d1da171ea8a983e18f1aa378b61d0254a57e72bdcf086c5ccf9d5c8eb2c1ef61

Observation 5ab458ae-beaa-450a-92ce-69c73a993689 · outbound

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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local_arxiv, observed 2026-08-08T16:20:51.724591Z

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source=pdf_text observed=2026-08-08T16:20:50.963293Z digest=sha256:ff2765e5848fbacca4cb90cafe1e397626170d38891a3d6eccc42ed72c13ed97

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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source=pdf_text observed=2026-08-08T16:20:50.967448Z digest=sha256:9dde99d63bf8a8e7686dc54c6697973ad230e8070face27197dcb07a31baa6aa

Observation 03700b4e-43ef-4eab-8161-1c0a7e9e79b0 · outbound

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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no resolver link, observed 2026-08-08T16:20:50.971327Z

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source=pdf_text observed=2026-08-08T16:20:50.971327Z digest=sha256:ab653684f8727a34bb1df5f8a7f00e02597131bb65ceb6145da7411628d1eefd

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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source=pdf_text observed=2026-08-08T16:20:50.975335Z digest=sha256:a8fd5a9f966a72a7a807919f35d707f9f6512ac9307bea6faeccf4d811195195

Observation 6bc631d2-b2ad-44b2-bd37-8678b9e4fb7b · outbound

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

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raw_fallback, observed 2026-08-08T16:20:51.944929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:50.979065Z digest=sha256:1c90f3ea7a760d8bf94f9d5fc7b7de178243031af0daebc9b9bb4423d42e9e6d

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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raw_fallback, observed 2026-08-08T16:20:51.935620Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:50.982835Z digest=sha256:b2bbfefb11ab77db1ef5be231e04eba6a8a696a168c431a99624058dda5db117

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

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raw_fallback, observed 2026-08-08T16:20:51.926028Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:50.986487Z digest=sha256:58b84c5c482bf850add655cede067b874809387e51ed83185eec15cd088ebd3f

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

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raw_fallback, observed 2026-08-08T16:20:51.916277Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:50.990272Z digest=sha256:e591ba54b7e95be75b98fcff749e994099417ec0f8f287d2ceadb8e1104f2b66

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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source=pdf_text observed=2026-08-08T16:20:50.993777Z digest=sha256:53b1b0185af11742721a24e10c5ea5f45bf09d9dd5538997e1afd57be656761c

Observation 10fb0f24-e93f-451f-8fab-1dc6f365b1a3 · 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 45

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source=pdf_text observed=2026-08-08T16:20:50.997457Z digest=sha256:5d10edaacccc89195fbe5e27002d0db124778cb63ada1c1d6c91367818963206

Observation f1271457-2f23-4fb5-b6a6-44b6f432e962 · outbound

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

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raw_fallback, observed 2026-08-08T16:20:51.893561Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.001072Z digest=sha256:0a48d53df34d398cc7b0792d5a1ed820ca11b8d34f0f0c5091642fff8961d878

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=pdf_text observed=2026-08-08T16:20:51.004649Z digest=sha256:a3fb2dba88a8c93e230d351c953ad9286e1739e8ec871952573bea2cdfc163b5

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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source=pdf_text observed=2026-08-08T16:20:51.008445Z digest=sha256:c895689342ef91e32987a7baaff903ebc4ccf0a632710e223449344e150b686e

Observation a40026f4-dac6-4a4d-bcd5-39f5704d05e9 · outbound

This paper cites Rixner, P.-S.

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.012298Z digest=sha256:da66b24b14ea6978e8f2971cdaaf2b0e3884f90304bcc9638746b0964ab52772

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=pdf_text observed=2026-08-08T16:20:51.015942Z digest=sha256:2bce865d25356c0ac790dbe8c41ea7ba7a632e01ad640445cef6eb4665dd2e31

Observation 52a63c72-10aa-4461-a4c2-48b800609c67 · 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 51

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raw_fallback, observed 2026-08-08T16:20:51.883626Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.019934Z digest=sha256:35298bd8a96b5440e419c9338a14b7f836d69b5be2467d77cc37b411f3059e47

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

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local_arxiv, observed 2026-08-08T16:20:51.365178Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.023411Z digest=sha256:e49fdb0ddb89d1bb591c25da94381496e517f065887304afe6978d80df7f5e91

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

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raw_fallback, observed 2026-08-08T16:20:51.874148Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.027169Z digest=sha256:42c72f2cb4a981b664989cb7d52559e011e183eb81e5a1e8e2f5e546c44632d5

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

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source=pdf_text observed=2026-08-08T16:20:51.030625Z digest=sha256:387c4445170ae15e8db2e4ad836cb8485bd9363b9f1a47c50e4aab2b10757892

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

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raw_fallback, observed 2026-08-08T16:20:51.859250Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.033973Z digest=sha256:0e27d796e6a49351aa75c3b3a80e3eb52415d1c4c24f1426109dcc8239e819b4

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

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

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source=pdf_text observed=2026-08-08T16:20:51.037441Z digest=sha256:90e0810a5ea37b8eea6713baf2d1382552cfcdbef887b389eafa662155168dbd

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

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raw_fallback, observed 2026-08-08T16:20:51.848982Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.041190Z digest=sha256:5327361238e817a926ce2e9e66342abd84c67df52d10416e105fe2547c0e561f

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

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source=pdf_text observed=2026-08-08T16:20:51.044668Z digest=sha256:af9b86521c4ada7b85b53e147e138b4761828fa2c4d60b4fe956d92ab4402727

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

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raw_fallback, observed 2026-08-08T16:20:51.328081Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.048696Z digest=sha256:b56a4763655820329470dd64c4cdc38a10666aa5d2f8c737f2d55509990b2e62

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

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raw_fallback, observed 2026-08-08T16:20:51.838287Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.052180Z digest=sha256:917ee19da3370bd144ba56036afe18a960464c24b28bdc519fb41288f9c5bdd4

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

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doi, observed 2026-08-08T16:20:51.124344Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.055752Z digest=sha256:e30006a7499785bca3b0d484f4701ea44d60fca9bb9e1c02d83b9cbdc275d72e

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

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

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source=pdf_text observed=2026-08-08T16:20:51.059335Z digest=sha256:752ba12da0c9904bb5084ea7f76c14baeece4a7658d483cdf732557a7b49c85c

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

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raw_fallback, observed 2026-08-08T16:20:51.821528Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.062493Z digest=sha256:6e38ef47224b14e32ec4142f60068a50d6d744dc4987b722ecddcc035f583733

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.065777Z digest=sha256:31da770895a62b17e32b00ea969d69f1c0b3abb078e2464d5928b08cfdd46b9e

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.068888Z digest=sha256:886c472044c8c6b8a07d27b01a65d3b3d394e154544d805a36b1e2c261387f9c

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.072100Z digest=sha256:884ef0a68b36803ef7e0aa4ba9e1ee0cdd6b7c96012b615c27ed9183e8b06ff6

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:20:51.075574Z digest=sha256:97279e69c98a4be0f0738b114cb807dd58f089ebf4be1897a553b528b5469061

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.078871Z digest=sha256:9a4767b4ec67b9775b2de0383fc8536ff2973230ca16766dced6e439ea3ea69e

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

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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T16:20:51.082011Z digest=sha256:db288c537ef7a7f0d9985ec6a3c772a70241969fa75b5a36a0168ff54e806624

Pith citing papers

No inbound Pith citation observations are available.