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Paper Citation Record · LEDGER

A posteriori analysis of neural network approximations

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2507.06017.

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

pith.paper-citation-record.v1
2507.06017 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:21:24.892199Z

measured 33 of 33 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T15:56:53.406744Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:29:35.961875Z

Reference resolution

32 of 32 outbound references displayed

  • verified exact2
  • verified fuzzy26
  • unresolved3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4164db2c-4e2c-4993-ac5b-eeb98fd52bc6 · outbound

This paper cites Aurada, M.

A posteriori analysis of neural network approximations Aurada, M

Reference 1

Resolution
verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.746587Z digest=sha256:9978c35241ff0ed773e81ddd6da2dd438b909b97b69ecc27fc1f10c16f02a10b

Observation 24e65089-3df6-4037-8cf1-b329f24e4591 · outbound

This paper cites Enforcing dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks.

A posteriori analysis of neural network approximations Enforcing dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks

Reference 2

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verified fuzzy
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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 dc1e9e12-9df4-4e7f-8a0c-bbacb6debcd7 · outbound

This paper cites Bochev and Max D.

A posteriori analysis of neural network approximations Bochev and Max D

Reference 3

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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 34104b0e-995c-4556-9382-906186fa6356 · outbound

This paper cites Finite element interpolated neural networks for solving forward and inverse problems.

A posteriori analysis of neural network approximations Finite element interpolated neural networks for solving forward and inverse problems

Reference 4

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.760816Z digest=sha256:7fcd3e22a6c0ea5609155653acfeaeec15742773546a63ca6b42ba239e95d31c

Observation 3b036cb3-892d-4615-847a-25339ffbb802 · outbound

This paper cites Numerical solution of inverse problems by weak adversarial networks.

A posteriori analysis of neural network approximations Numerical solution of inverse problems by weak adversarial networks

Reference 5

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verified fuzzy
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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 9b3d3664-0012-409f-bbec-c949dd423804 · outbound

This paper cites Deep least-squares methods: An unsupervised learning-based numerical method for solving elliptic pdes.

A posteriori analysis of neural network approximations Deep least-squares methods: An unsupervised learning-based numerical method for solving elliptic pdes

Reference 6

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verified fuzzy
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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 f07c2d21-0382-4717-998b-ae66f437cb66 · outbound

This paper cites A posteriori error control for DPG methods.

A posteriori analysis of neural network approximations A posteriori error control for DPG methods

Reference 7

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.775614Z digest=sha256:0b45eaf1c16cc38758186adbc5382bc925067b09915b95fe4538dd876aad5d6d

Observation 95a093e8-90ef-4a04-ab71-aacae7617e65 · outbound

This paper cites Carstensen, L.

A posteriori analysis of neural network approximations Carstensen, L

Reference 8

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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.

source=arxiv_source observed=2026-08-06T19:21:24.780993Z digest=sha256:24df968d8d55ea781d9c00c17c9333622da7d8ebb31f1bb0effdd4ce3a4b1f9e

Observation 7db17621-6b2e-426a-a410-d0b0fda5a791 · outbound

This paper cites Demkowicz and J.

A posteriori analysis of neural network approximations Demkowicz and J

Reference 9

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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.

source=arxiv_source observed=2026-08-06T19:21:24.785813Z digest=sha256:e8aeb8e060b20b70225e2dd49eb9aaf9c555684f2740a5a883c83856d49611e7

Observation 8306a96a-98ad-4b04-abfe-dfef82baf7c6 · outbound

This paper cites The discontinuous petrov–galerkin method.

A posteriori analysis of neural network approximations The discontinuous petrov–galerkin method

Reference 10

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.790152Z digest=sha256:f56d982c87fe553376109fc914dacbcd0bb61cea3cc63c418d503d3abfa91f00

Observation 84ee0571-628c-4a28-a154-4014fb93e057 · outbound

This paper cites Equivalence of local- and global-best approximations, a simple stable local commuting projector, and optimal hp approximation estimates in H( div ).

A posteriori analysis of neural network approximations Equivalence of local- and global-best approximations, a simple stable local commuting projector, and optimal hp approximation estimates in H( div )

Reference 11

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.794714Z digest=sha256:5ed9c5708b7f05b5a1f50d58eeacd459551021e868e7fef8ff17170e80fb93d2

Observation 8df1ef56-a42e-4910-987b-5db616aabb9c · outbound

This paper cites A posteriori certification of PDE approximations with particular application to neural networks.

A posteriori analysis of neural network approximations A posteriori certification of PDE approximations with particular application to neural networks

Reference 12

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verified exact
local_arxiv, observed 2026-08-06T19:21:25.147061Z

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 3e858f29-ea35-48ca-bdd8-cda56c70786b · outbound

This paper cites Multilevel decompositions and norms for negative order S obolev spaces.

A posteriori analysis of neural network approximations Multilevel decompositions and norms for negative order S obolev spaces

Reference 13

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verified fuzzy
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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 7794e2f2-5fce-4c85-a6f6-0a7e58fe4525 · outbound

This paper cites Hiptmair.

A posteriori analysis of neural network approximations Hiptmair

Reference 14

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.810036Z digest=sha256:40af46cf5116c977ddf830a1f5510cf54a3229c54b8d012cab15559f97ea29bd

Observation 45f98d43-577f-45b9-9430-6840e2fc41e5 · outbound

This paper cites Characterizing possible failure modes in physics-informed neural networks.

A posteriori analysis of neural network approximations Characterizing possible failure modes in physics-informed neural networks

Reference 15

Resolution
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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.

source=arxiv_source observed=2026-08-06T19:21:24.814619Z digest=sha256:5951a3531ee542661c426e1af9ba8835fe33b9d46760f7fd9292028f25ad7c92

Observation f2994b91-9ea1-458d-8558-1e606b8f61e3 · outbound

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

A posteriori analysis of neural network approximations Variational Physics-Informed Neural Networks For Solving Partial Differential Equations

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:21:24.819465Z digest=sha256:631d5d3a662770b79908086a881926ca07494e90471ee00971a9b25077b81ed7

Observation d1003254-5e3b-4e0d-a7a3-05989069ef1d · outbound

This paper cites hp-vpinns: Variational physics-informed neural networks with domain decomposition.

A posteriori analysis of neural network approximations hp-vpinns: Variational physics-informed neural networks with domain decomposition

Reference 17

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verified fuzzy
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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 b127a9d7-7f6d-4886-ae9b-be4fefa38f62 · outbound

This paper cites Deep learning.

A posteriori analysis of neural network approximations Deep learning

Reference 18

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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 c03d861e-50d6-49f1-89ca-d890ab8a3b6a · outbound

This paper cites Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease.

A posteriori analysis of neural network approximations Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease

Reference 19

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local_arxiv, observed 2026-08-06T19:21:25.096409Z

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 610b7bc9-9b75-4b30-a2b8-25d5fdaa7394 · outbound

This paper cites Minimal residual methods in negative or fractional S obolev norms.

A posteriori analysis of neural network approximations Minimal residual methods in negative or fractional S obolev norms

Reference 20

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.838673Z digest=sha256:4861e73d7f3b01b9f2cbe9c816fa69cce8119575a3447e368760dccb5e3b2cd3

Observation fc5bc07e-0794-4852-beae-b4e423175ab6 · outbound

This paper cites Multilevel finite element approximation.

A posteriori analysis of neural network approximations Multilevel finite element approximation

Reference 21

Resolution
verified fuzzy
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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 f9614e1b-3475-4771-ac10-e9f635fb0682 · outbound

This paper cites Robust variational physics-informed neural networks.

A posteriori analysis of neural network approximations Robust variational physics-informed neural networks

Reference 22

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.847667Z digest=sha256:ee139592c05bc9a87954dac790c25a00f12eace8f188cb11d2d7edc537eddd64

Observation 7d395cae-d985-43ed-b5da-ad79d7cec98c · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

A posteriori analysis of neural network approximations Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 23

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:21:24.852521Z digest=sha256:952d5a42ad961035823b17ee76ffbd812e8eb2ea0b27961386234ea369650552

Observation b980d9ab-b18c-410d-8232-fbcae136dc15 · outbound

This paper cites Stephan and Thanh Tran.

A posteriori analysis of neural network approximations Stephan and Thanh Tran

Reference 24

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verified fuzzy
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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 74d0e47e-fb30-435c-9431-beaf484e8375 · outbound

This paper cites Uniform preconditioners for problems of negative order.

A posteriori analysis of neural network approximations Uniform preconditioners for problems of negative order

Reference 25

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verified fuzzy
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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 de5b71d1-1f0d-4047-adf3-02dc9940d7c5 · outbound

This paper cites Uniform preconditioners for problems of positive order.

A posteriori analysis of neural network approximations Uniform preconditioners for problems of positive order

Reference 26

Resolution
verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.865382Z digest=sha256:00851fea1727e17676c7b5768ceece732c943a35820d0121d1edb4a42ff6924e

Observation b23fbc92-be0d-4893-a31c-d6a492c6b380 · outbound

This paper cites A deep fourier residual method for solving pdes using neural networks.

A posteriori analysis of neural network approximations A deep fourier residual method for solving pdes using neural networks

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:21:25.243372Z

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=arxiv_source observed=2026-08-06T19:21:24.869793Z digest=sha256:26ec4d910febc8d5fbbab2d6202e6a4d0745dff76a52e24c237cae0d3bedc267

Observation 26ee1b2b-cfc9-4445-a8cb-679eff371370 · outbound

This paper cites Optimizing variational physics-informed neural networks using least squares.

A posteriori analysis of neural network approximations Optimizing variational physics-informed neural networks using least squares

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:21:25.219589Z

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=arxiv_source observed=2026-08-06T19:21:24.874827Z digest=sha256:6c49fd30b2a871567f11d335b86a8a1219aa1d713718630e87c9f5ac631736d9

Observation f559c938-5c74-4d17-9c22-140eef2fc189 · outbound

This paper cites Neural network methods for power series problems of perron-frobenius operators.

A posteriori analysis of neural network approximations Neural network methods for power series problems of perron-frobenius operators

Reference 29

Resolution
verified exact
raw_fallback, observed 2026-08-06T19:21:25.071951Z

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=arxiv_source observed=2026-08-06T19:21:24.879283Z digest=sha256:58857bee1b6ed2c9bf1684f552a9258c69222cb589abbab48780264b8bb2575f

Observation 8b557896-4ce4-4600-9774-f94c34a4b600 · outbound

This paper cites Verf\"urth.

A posteriori analysis of neural network approximations Verf\"urth

Reference 30

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verified fuzzy
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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.

source=arxiv_source observed=2026-08-06T19:21:24.883563Z digest=sha256:636b55eb31fb466ced48c3c28de7ba2b45f610c56eeb3c05419bdafbecfa29de

Observation 34fd85d4-8e98-4240-9092-0d3213d8d194 · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.

A posteriori analysis of neural network approximations When and why pinns fail to train: A neural tangent kernel perspective

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T19:21:24.887870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:21:24.887870Z digest=sha256:6660f143fd64c4dbf1c8f148ed889505fb06aa0d6a9d52e8159e46fc57d65dcb

Observation 247b1c16-6753-432a-899a-21ca6506fdc9 · outbound

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

A posteriori analysis of neural network approximations The deep ritz method: a deep learning-based numerical algorithm for solving variational problems

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:21:25.167055Z

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=arxiv_source observed=2026-08-06T19:21:24.892199Z digest=sha256:e1874f99569d2554e7439c282fe0bd627883dc5fd3f797843cd0655e6c85351b

Pith citing papers

Observation 09785a76-fec8-432e-a3fd-9fa5ec9a6b3a · inbound

Neural network approximation in discrete dual norms with adaptive test spaces cites this paper.

Neural network approximation in discrete dual norms with adaptive test spaces A posteriori analysis of neural network approximations

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:29:35.963688Z

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=arxiv_source observed=2026-06-26T15:56:53.406744Z digest=sha256:bba82b6081176868dab9e62f40cbf609bb5632a4b9942a9a78c8c9ec1a39edfd