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Paper Citation Record · LEDGER

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement

As of 9 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.14665.

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

pith.paper-citation-record.v1
2607.14665 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:32:23.864199Z

measured 15 of 15 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.

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

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1107ad77-38fc-4940-a8c0-ef166dbdb358 · outbound

This paper cites Journal of Computational Physics , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Journal of Computational Physics , volume =

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:22.261844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:22.261844Z digest=sha256:6918f431fd2c60efae31fd37bc38fb5f664e87e69dba19f394c533a64fe54dde

Observation ae64a29a-70c7-400a-a97b-fa9984e491bd · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Computer Methods in Applied Mechanics and Engineering , volume =

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:22.398811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:22.398811Z digest=sha256:d32bdc85b9eecb80e34504e0c07a2ed0f47aa60853056d4e4790e8545466113b

Observation fa273fc1-4393-4785-a750-380261c70b12 · outbound

This paper cites and Gholami, Amir and Zhe, Shandian and Kirby, Robert and Mahoney, Michael W.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement and Gholami, Amir and Zhe, Shandian and Kirby, Robert and Mahoney, Michael W

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:22.558802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:22.558802Z digest=sha256:8d146a5c37ef427c3e511b3ce94600fc1d226250a6fcd8a83a86456e1a85dbb7

Observation 201291aa-6a54-4859-b9db-3b197cd29481 · outbound

This paper cites Journal of Computational Physics , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Journal of Computational Physics , volume =

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:22.742753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:22.742753Z digest=sha256:53eb8c5e0562773cb0f3488299256c3b06f96837aba9f017d8afe172e3e5f69c

Observation b6587055-e4c5-4e02-9df0-eb2f0af03018 · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Computer Methods in Applied Mechanics and Engineering , volume =

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:22.921938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:22.921938Z digest=sha256:4892c841b0fe77d15b8c49823887f6a3e3d3ad00a0a94049cd325d1898612859

Observation c4980f34-7c04-43d2-825f-e33f7012b1fd · outbound

This paper cites PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.088920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.088920Z digest=sha256:360ce507fb7600e150eedd16dcaee6fc1e7cf5985fd1ceb47c8fa2771ea72bf1

Observation d33f12ac-c599-41f2-be3a-4df7493697d0 · outbound

This paper cites and Karniadakis, George Em , title =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement and Karniadakis, George Em , title =

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.287615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.287615Z digest=sha256:074e2b675ccb81370bf44dcc20497e309fdee74e51c4354104b3cd5567092d72

Observation 10d344a6-64b9-465f-8672-12dcd1188b98 · outbound

This paper cites and Kharazmi, Ehsan and Karniadakis, George Em , title =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement and Kharazmi, Ehsan and Karniadakis, George Em , title =

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.364108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.364108Z digest=sha256:98ff78e032e2e176f7d74f18622e61412d8dbb7414462a4044c2e0554b876377

Observation edfdbf7f-c9ce-4707-bd94-3231aa33608a · outbound

This paper cites and Karniadakis, George Em , title =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement and Karniadakis, George Em , title =

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.421084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.421084Z digest=sha256:11fc1c78b0973fe4402db13d13d83f019a0863e41f54639404ab4d3279b3bf6f

Observation e5596624-86cb-4421-b407-c1cd94a16d08 · outbound

This paper cites Communications in Computational Physics , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Communications in Computational Physics , volume =

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.498294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.498294Z digest=sha256:a2656f5d2453246120dd7ac819a220d997a0954b97a4f54fe2ce099652073ec2

Observation 2ecf35b9-190b-4891-9c6c-86f0d92e362d · outbound

This paper cites SIAM Review , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement SIAM Review , volume =

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.581701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.581701Z digest=sha256:c68334620a5392e8f9bf4d196971fd97a745435ad392e048793bc397a1358c7b

Observation fbfce823-7afa-4bde-8a8f-35bded91c5cb · outbound

This paper cites Computer Methods in Applied Mechanics and Engineering , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Computer Methods in Applied Mechanics and Engineering , volume =

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.610248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.610248Z digest=sha256:c881584052c9000b06abb6f3f20a018c97ffa8ffd7bca062525bb02d5e2f1632

Observation f9cc04bc-edfb-4b95-83ed-8694edff8948 · outbound

This paper cites SIAM Journal on Scientific Computing , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement SIAM Journal on Scientific Computing , volume =

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.675408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.675408Z digest=sha256:0a4eb92fdbc22d496ff66991c4e23d427d99ab3af49e328ac0d25cede1fd6b51

Observation 5072381f-7805-4718-8d85-962e13d57d87 · outbound

This paper cites Journal of Computational Physics , volume =.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement Journal of Computational Physics , volume =

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.766316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.766316Z digest=sha256:1435154d53d964492b988beb42932d3aa0cd226df1c34695fb0fe6148ac0daa0

Observation ede3beb4-9a3d-478f-ad81-180ebc48c9ec · outbound

This paper cites and Braga-Neto, Ulisses M.

A new strategy for physics-informed neural networks based on hierarchical collocation point refinement and Braga-Neto, Ulisses M

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:23.864199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T01:32:23.864199Z digest=sha256:55f6d5f06f601670dbfd6963c272ed337bab2c2747dff8d007e20ac76b3ad80b

Pith citing papers

No inbound Pith citation observations are available.