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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-10T06:31:04.303077+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:f644a7a5cc627a1f707a59ca807578b078c63ac25bd5db9f0ed0d553e432ce94

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=sha256:29da67a856ff92651877305847d4408a324e9d12912223ea0e90597a89f7e111

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=sha256:2db17988630f4f08e190ff75b48c1ff99f855b107272af65bc6a741bd153f6a8

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=sha256:28916579c39c26e0ef3bfe5d562ee1fe4bb08f4dbcd22301e4415207d4b2b4c9

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=sha256:16e5073edf94b68049fbd4130d570788cfd4fd89d32d1c7a92046ea22f8cfdaf

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=sha256:4229de5f48dfece9539719cef215d2d108e3e6591f2b453418625ae408c28d0f

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=sha256:5215d07a75257804d7eb1ea03af318d154a42aa0ddf95f1f5f0f5f13a780fc11

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=sha256:65261a6e647dd78d73615d9f1595a363e4eda6f1ae07fdac21662a36f6034e7c

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=sha256:97cd88a7f294a4cf5fdb8eeb82c0a5dbf6da563e2ea2a7b1451064f41f96f20e

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=sha256:4a84afac1f80147eac35c69c4d4f76282b759ee4bceb61e3ba2ea687bf182269

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=sha256:55753898f00505413cd80fbc7bb24c5ed83fb6ed50c4c44cf25a98846d685cd7

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=sha256:314b82c152e88911d88785e351d7c7ac159c87b3e2efa01695f2944bef666cf3

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=sha256:195fecce9414b98816aafef5901ca14f432357db5b15d01b6b2d081fedf40897

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=sha256:532ecd43dba74568e49b23936a39c0a44f36ef1e2803107a14aa6965618fb3cc

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:eb10c73945a44b30ce16452eebd75e71f9176f1d42b3683349ceccdc53003711

Pith citing papers

No inbound Pith citation observations are available.