Paper Citation Record · LEDGER

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation

As of 9 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2603.06152.

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

pith.paper-citation-record.v1
2603.06152 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T13:59:33.352526Z

measured 13 of 13 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 2026-07-15T13:59:33.352526Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact4
verified fuzzy0
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74f75fe1-3754-4ed4-b17b-c3e2789e593c · outbound

This paper cites Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a43a7897-4ee7-423b-8c8c-cceec868e7cf · outbound

This paper cites Governing equations of non-hydrostatic model117 In this study, a single-layer non-hydrostatic free surface model is adopted118 for our simulations.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Governing equations of non-hydrostatic model117 In this study, a single-layer non-hydrostatic free surface model is adopted118 for our simulations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ed07204-d317-42c8-a003-204db96c33b2 · outbound

This paper cites Following the pioneering work and relevant extensions of layer-206 averaged non-hydrostatic approach (Stelling and Zijlema, 2003; Wei and Jia,207.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Following the pioneering work and relevant extensions of layer-206 averaged non-hydrostatic approach (Stelling and Zijlema, 2003; Wei and Jia,207

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcb5894b-1957-4fe8-8ee0-c17cb130548d · outbound

This paper cites (2019) integrated the layer-209 averaged non-hydrostatic approach into DG finite element implementation in210 Thetis.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation (2019) integrated the layer-209 averaged non-hydrostatic approach into DG finite element implementation in210 Thetis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a3efe6d-74fb-4ce4-be6e-444b1dd8ef65 · outbound

This paper cites Monge–Ampère mesh movement 249 Following the work of McRae et al.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Monge–Ampère mesh movement 249 Following the work of McRae et al

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afd9f5bd-d57b-428b-aa7c-f3ed0a0f81ad · outbound

This paper cites N-wave strip source 413 This problem was introduced in Kanoğlu et al.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation N-wave strip source 413 This problem was introduced in Kanoğlu et al

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cc608c5-72df-4a44-86de-74d87df7a4a4 · outbound

This paper cites an unresolved cited work.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-15T13:59:33.352526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e2d129-628f-425e-aa5a-2731fdb40499 · outbound

This paper cites an unresolved cited work.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Unresolved cited work

Reference 8

Resolution: verified exact; doi, observed 2026-07-15T14:01:34.364855Z

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.

Observation 51d99b4e-6253-4289-809f-62b2db995f31 · outbound

This paper cites Technical Report ANL-21/39 - Revision666 3.24.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Technical Report ANL-21/39 - Revision666 3.24

Reference 9

Resolution: verified exact; doi, observed 2026-07-15T14:01:34.384656Z

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.

Observation 36b92adf-1485-44b5-9654-df126dbfb8ab · outbound

This paper cites Improved efficiency of a non-681 hydrostatic, unstructured grid, finite volume model.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Improved efficiency of a non-681 hydrostatic, unstructured grid, finite volume model

Reference 10

Resolution: verified exact; doi, observed 2026-07-15T14:01:34.374707Z

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.

Observation 9ea42e4e-cbdd-4c4f-8010-71ca5463d512 · outbound

This paper cites G-Adaptivity: optimised graph-based mesh relocation for finite element methods.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation G-Adaptivity: optimised graph-based mesh relocation for finite element methods

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-07-15T14:01:34.380205Z

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.

Observation 375300cc-1440-479e-bf02-a70fda7db6ef · outbound

This paper cites Newark,775 Delaware.

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation Newark,775 Delaware

Reference 12

Resolution: verified exact; doi, observed 2026-07-15T14:01:34.369475Z

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.

Pith citing papers

Observation 74f75fe1-3754-4ed4-b17b-c3e2789e593c · inbound