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Paper Citation Record · LEDGER

Review of multi-fidelity models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1609.07196.

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

pith.paper-citation-record.v1
1609.07196 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:25:55.283842Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T15:09:36.818046Z

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 526288da-707f-4b4b-8d88-ec7267f7457e · inbound

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty cites this paper.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Review of multi-fidelity models

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-24T15:09:36.820833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-24T15:08:15.295658Z digest=sha256:972841e5f09d93f02edb780d72090ca4762802790f4ddafc754bc399697b362f

Observation d307959c-6c6c-4fec-931e-c848131ec3fd · inbound

Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures cites this paper.

Multi-fidelity Bayesian Data-Driven Design of Energy Absorbing Spinodoid Cellular Structures Review of multi-fidelity models

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T14:25:55.283842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:25:55.283842Z digest=sha256:6599074fe6ffdccea37532bc31da98975a6233660016d62dc7d5ba622e59590e

Observation 3d1d41d0-c17c-452a-a8b0-8f8d4c37d9ba · inbound

Multiport Analytical Pixel Electromagnetic Simulator (MAPES) for AI-assisted RFIC and Microwave Circuit Design cites this paper.

Multiport Analytical Pixel Electromagnetic Simulator (MAPES) for AI-assisted RFIC and Microwave Circuit Design Review of multi-fidelity models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-03T20:05:39.883542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:05:39.883542Z digest=sha256:18b9af042b7d8f7186561d5aff719bcf7ecdfb4db2adc7422a1fdded8aae1e48

Observation 0bb30e1e-15e2-42e3-9614-24962e373b90 · inbound

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data cites this paper.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Review of multi-fidelity models

Reference 14

Resolution
malformed identifier
no resolver link, observed 2026-07-13T20:27:45.196743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T20:27:45.196743Z digest=sha256:47e1021942ee3630d3956118f7fa5ffb9263d295f261a4129171344b1f6548ef

Observation 3f1d0b4e-f377-4a5a-b748-7a7099a59470 · inbound

Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks cites this paper.

Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks Review of multi-fidelity models

Reference 36

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:01:15.899128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T01:58:11.576425Z digest=sha256:19791c51544788ad505392e053472e1c23157cafee9fbb7c7d3040fbc40527ad

Observation 361fab06-9c51-4aaa-b77d-aae656c379a1 · inbound

Multi-Fidelity Quantile Regression cites this paper.

Multi-Fidelity Quantile Regression Review of multi-fidelity models

Reference 71

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:16:25.225968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-12T05:14:15.478876Z digest=sha256:b04ab8a78b259cd6dec670eb18d514c198efba9cefa0e02c06d237e566b99b0d