Paper Citation Record · LEDGER

Physics-informed neural networks for inverse problems in supersonic flows

As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2202.11821.

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

pith.paper-citation-record.v1
2202.11821 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:59:44.978667Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T23:34:04.529381Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e71b1be3-913e-4979-8786-effb09f4976e · inbound

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows cites this paper.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed neural networks for inverse problems in supersonic flows

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T14:59:44.978667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79a74597-3c30-4d3a-9673-9bc09c36a94c · inbound

A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades cites this paper.

A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades Physics-informed neural networks for inverse problems in supersonic flows

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:30:58.604480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 258e9b69-d36d-43c9-b467-c049b1b7b1df · inbound

A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades cites this paper.

A fast Physics-Informed Neural Networks based approach to the 2D design of turbine blades Physics-informed neural networks for inverse problems in supersonic flows

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:45:26.498457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ae86db2-4ce7-4bf3-977f-1d35065dd0f5 · inbound

Sampling Distributions as Regularization in Learned Inverse Problems cites this paper.

Sampling Distributions as Regularization in Learned Inverse Problems Physics-informed neural networks for inverse problems in supersonic flows

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:34:04.530688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.