Paper Citation Record · LEDGER

Deep Operator Networks for Bayesian Parameter Estimation in PDEs

As of 16 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2501.10684.

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

pith.paper-citation-record.v1
2501.10684 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:06:10.272981Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact6
verified fuzzy5
unresolved26
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de3e347f-7082-4b04-a16f-76e8f549709d · outbound

This paper cites Roberts, A.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Roberts, A

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:10.066704Z

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Observation b42efd95-e68f-4888-a84a-75e09e05de9a · outbound

This paper cites Sarshar, P.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Sarshar, P

Reference 2

Resolution: verified exact; doi, observed 2026-08-10T19:06:10.758394Z

Source-reported events for the cited work

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Observation 7afb5ee6-2098-4e96-9752-9164bfec33ba · outbound

This paper cites Optimal Control Via Neural Networks: A Convex Approach.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Optimal Control Via Neural Networks: A Convex Approach

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:10.077947Z

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Observation be3f0c29-f575-4a24-b778-309f422a1e7b · outbound

This paper cites Solving PDE-constrained Control Problems Using Operator Learning.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Solving PDE-constrained Control Problems Using Operator Learning

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:06:10.710733Z

Source-reported events for the cited work

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Observation fe88ee91-a964-4e27-a6b4-db9cf1416bbe · outbound

This paper cites Fast PDE-constrained optimization via self-supervised operator learning.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Fast PDE-constrained optimization via self-supervised operator learning

Reference 5

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Observation 750f104b-6c0e-48a2-8ac3-bd1096a2ba54 · outbound

This paper cites Bhattacharjee, A.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Bhattacharjee, A

Reference 6

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Observation 2a1969c6-de9d-4fff-98ab-0e2f0d08ac19 · outbound

This paper cites doi:10.1016/j.jcp.2018.10.045.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs doi:10.1016/j.jcp.2018.10.045

Reference 7

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Source-reported events for the cited work

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Observation 86892a5a-6651-415b-82fb-9c404cf3615b · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 8

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Observation b380e26d-f152-41ca-89d0-533d0408cb09 · outbound

This paper cites doi:10.1007/s10915-022-01939-z.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs doi:10.1007/s10915-022-01939-z

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:10.118409Z

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Observation 609cb9cf-37b5-4871-b912-c4edb17d457b · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 10

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Observation 91b57ec9-7afb-4ee0-9e9b-d78508f7b2ca · outbound

This paper cites doi:10.1137/18m1229845.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs doi:10.1137/18m1229845

Reference 11

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Source-reported events for the cited work

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Observation 25652451-7597-407f-b894-1a5c1f72cc8e · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:10.135929Z

Source-reported events for the cited work

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Observation 177f2d35-6b34-4a71-bf07-0f57f423d4ca · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation 1fb4645d-5b0b-48e4-a543-199ff2aa4dc0 · outbound

This paper cites Physics-Informed Neural Nets for Control of Dynamical Systems.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Physics-Informed Neural Nets for Control of Dynamical Systems

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:06:10.561620Z

Source-reported events for the cited work

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Observation 91da3d44-5c7b-4144-b19f-d321b571eebd · outbound

This paper cites Physics-informed neural networks for PDE-constrained optimization and control.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Physics-informed neural networks for PDE-constrained optimization and control

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:06:10.536364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3c2a198b-bd9c-425f-aa98-2faa6bb103d2 · outbound

This paper cites Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:06:10.495792Z

Source-reported events for the cited work

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Observation 152903ef-94b3-42fb-8e20-9ef2a5520d8b · outbound

This paper cites Zhang, L.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Zhang, L

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:10.164861Z

Source-reported events for the cited work

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Observation 857d5266-b60d-4a9b-b399-92f6019a2f43 · outbound

This paper cites Paszke, S.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Paszke, S

Reference 18

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Source-reported events for the cited work

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Observation 8290416b-36f9-4665-a6a6-ab0c20631cfc · outbound

This paper cites Bradbury, R.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Bradbury, R

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:11.900902Z

Source-reported events for the cited work

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Observation 2503401d-8e0e-4c8b-8f10-43390df8461a · outbound

This paper cites Abadi, et al., Tensorflow: Large-scale machine learning on heteroge- neous systems, software available from tensorflow.org (2015).

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Abadi, et al., Tensorflow: Large-scale machine learning on heteroge- neous systems, software available from tensorflow.org (2015)

Reference 20

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Observation b1d21e02-2e0f-41dc-bb8b-5860a4842089 · outbound

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 21

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 22

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This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 23

Resolution: verified exact; doi, observed 2026-08-10T19:06:10.435959Z

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Observation bca80d23-5004-4004-9e07-92fbb4b175d8 · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 24

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Observation 0a68c9b0-2fe8-442a-979c-70a7544f99a0 · outbound

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 25

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Observation 64adeb39-5541-481f-9b51-e228950da218 · outbound

This paper cites Wang, D.-Y.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Wang, D.-Y

Reference 26

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Observation 7cb08ac1-2841-42d5-b772-c73b46726982 · outbound

This paper cites Bonneville, C.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Bonneville, C

Reference 27

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Source-reported events for the cited work

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Observation f4e917d5-6b19-4ae8-90f0-68fbda727e26 · outbound

This paper cites What Are Bayesian Neural Network Posteriors Really Like?.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs What Are Bayesian Neural Network Posteriors Really Like?

Reference 28

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 29

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Observation d43bc6cc-af6e-4db5-8ab2-b5a20dc54d9c · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 30

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Observation 321a3984-20f8-41e4-91d5-4381cff95f6c · outbound

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 31

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Observation 934d65b7-0a0f-4fea-b9af-b55d8b727882 · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 32

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 33

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Observation fec83c3e-6941-4503-a5fb-c18c8a33f108 · outbound

This paper cites Kendall, Y.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Kendall, Y

Reference 34

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Observation c92c5f63-f35c-445e-a74c-85fa1fb901bc · outbound

This paper cites Srivastava, G.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Srivastava, G

Reference 35

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Observation e61a2a44-50ba-47db-9b3d-93b7038a51cc · outbound

This paper cites Xiang, W.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Xiang, W

Reference 36

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Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 37

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This paper cites URLhttps://is.gd/ZvwCFr.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs URLhttps://is.gd/ZvwCFr

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:11.835308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b41a330-298a-4a51-bf2c-0c98e3f932e4 · outbound

This paper cites Abdar, F.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Abdar, F

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:06:11.815350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bf05ca97-f85b-4ebf-a2a3-47ee7dcc728a · outbound

This paper cites an unresolved cited work.

Deep Operator Networks for Bayesian Parameter Estimation in PDEs Unresolved cited work

Reference 297

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Pith citing papers

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