Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:1907.11739.

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

pith.paper-citation-record.v1
1907.11739 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

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measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 0f86e99e-7bda-460e-8998-0f722a6bebe7 · outbound

This paper cites Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions

Reference 1

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Observation e80bb0cc-ccf5-49d2-a4d6-395ec6f6cd9c · outbound

This paper cites Surrogate-based analysis and optimization.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Surrogate-based analysis and optimization

Reference 2

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Observation b24b7811-bc33-40a2-b5d5-683b92bedda4 · outbound

This paper cites Special section on multidisci- plinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come? AIAA journal, 52(4):670–690.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Special section on multidisci- plinary design optimization: metamodeling in multidisciplinary design optimization: how far have we really come? AIAA journal, 52(4):670–690

Reference 3

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Observation 42c6b399-e1c4-491d-9dd2-b4a5504a93db · outbound

This paper cites On approaches to combine experimental strength and simulation with application to open-hole-tension conﬁguration.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty On approaches to combine experimental strength and simulation with application to open-hole-tension conﬁguration

Reference 4

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Observation 64b04c18-9504-4eec-93d8-981816015eb8 · outbound

This paper cites Bayesian uncertainty quantiﬁcation and information fusion in calphad-based thermodynamic modeling.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Bayesian uncertainty quantiﬁcation and information fusion in calphad-based thermodynamic modeling

Reference 5

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Observation 72ddf46a-3e11-4349-ab32-77abf8df931b · outbound

This paper cites Shabouei, W.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Shabouei, W

Reference 6

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Observation a41077a5-b6b4-4649-80c0-decfa521d5a7 · outbound

This paper cites Model reduction for large-scale systems with high-dimensional parametric input space.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Model reduction for large-scale systems with high-dimensional parametric input space

Reference 7

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Observation 6910adf0-fa7f-4abf-b9d9-213093ee7b59 · outbound

This paper cites Multi-ﬁdelity surrogate modeling for application/architecture co-design.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Multi-ﬁdelity surrogate modeling for application/architecture co-design

Reference 8

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Observation 941c42f1-715a-45b7-8db7-e84701607c38 · outbound

This paper cites Practical options for selecting data-driven or physics-based prognostics algorithms with reviews.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.277185Z

Source-reported events for the cited work

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Observation f8b2e3e9-8c60-46fb-a547-687a7f9347b6 · outbound

This paper cites Crashworthiness-based lightweight design problem via new robust design method considering two sources of uncertainties.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Crashworthiness-based lightweight design problem via new robust design method considering two sources of uncertainties

Reference 10

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Observation ab087898-c2a3-4893-aa56-50ec6414dacd · outbound

This paper cites A single-loop kriging surrogate modeling for time-dependent reliability analysis.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty A single-loop kriging surrogate modeling for time-dependent reliability analysis

Reference 11

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Observation 180ddca5-8b67-4afe-9dd5-1e325fb9a5cd · outbound

This paper cites Ex- perimental ﬂapping wing optimization and uncertainty quantiﬁcation using limited samples.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Ex- perimental ﬂapping wing optimization and uncertainty quantiﬁcation using limited samples

Reference 12

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Observation 16a66cc8-44d2-45cb-860f-ec97ac397216 · outbound

This paper cites Parallel surrogate-assisted global optimization with expensive functions–a survey.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Parallel surrogate-assisted global optimization with expensive functions–a survey

Reference 13

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Observation b59cc2d9-5fe5-426a-a01f-51617ae156e0 · outbound

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Unresolved cited work

Reference 14

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Observation 5dcd3330-0c27-4376-bd4b-0b7d771c740f · outbound

This paper cites Computational Approaches for Aerospace Design: The Pursuit of Excellence.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Computational Approaches for Aerospace Design: The Pursuit of Excellence

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.262498Z

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Observation dbe1e5d3-9c9e-4dee-ac92-4b9b26c4486c · outbound

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Unresolved cited work

Reference 16

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Observation e3f22409-03ec-4ea9-aed5-9aca626fd536 · outbound

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Unresolved cited work

Reference 17

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Observation a96afd73-5793-4e9e-949e-5ecfe1b723e3 · outbound

This paper cites Bayesian multi-source modeling with legacy data.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Bayesian multi-source modeling with legacy data

Reference 18

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Observation e14e5a49-75b8-4fb6-9d21-6f0fbd8477e5 · outbound

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Multi-source surrogate modeling with bayesian hierarchical regression

Reference 19

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Observation 71f951e1-d7a9-4b8c-8652-f8cba728aac5 · outbound

This paper cites Kennedy and Anthony O’Hagan.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Kennedy and Anthony O’Hagan

Reference 20

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Observation c5f40043-fda6-4efa-8cfc-4f97d6ebc4d2 · outbound

This paper cites Multiﬁdelity surrogate based on single linear regression.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Multiﬁdelity surrogate based on single linear regression

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.247226Z

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Observation 959be4c2-6252-42f5-980b-28ebb4fdb987 · outbound

This paper cites Remarks on multi-ﬁdelity surrogates.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Remarks on multi-ﬁdelity surrogates

Reference 22

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Observation 526288da-707f-4b4b-8d88-ec7267f7457e · outbound

This paper cites Review of multi-fidelity models.

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

Reference 23

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Observation 5961c712-afae-4c5a-b9cd-422f60b47f46 · outbound

This paper cites Multi-ﬁdelity Gaussian process regression for computer experiments.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Multi-ﬁdelity Gaussian process regression for computer experiments

Reference 24

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Observation 1be5522d-1e66-4b22-865c-d2f6819cb497 · outbound

This paper cites An approach to constructing nested space-ﬁlling designs for multi-ﬁdelity computer experiments.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty An approach to constructing nested space-ﬁlling designs for multi-ﬁdelity computer experiments

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.237687Z

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Observation 1c636b65-a457-4de4-8e01-8f5d8acb02e2 · outbound

This paper cites Difference mapping method using least square support vector regression for variable-ﬁdelity metamodelling.Engineering Optimization, 47(6):719–736.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Difference mapping method using least square support vector regression for variable-ﬁdelity metamodelling.Engineering Optimization, 47(6):719–736

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.234731Z

Source-reported events for the cited work

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Observation 4c473ae1-9ba7-488e-bd2f-a334a06d7b07 · outbound

This paper cites Sequential kriging optimization using multiple-ﬁdelity evaluations.Structural and Multidisciplinary Optimization, 32(5):369–382.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Sequential kriging optimization using multiple-ﬁdelity evaluations.Structural and Multidisciplinary Optimization, 32(5):369–382

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.299020Z

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Observation aad8057c-82bb-43d7-b079-fb323893096c · outbound

This paper cites Multiﬁdelity importance sampling.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Multiﬁdelity importance sampling

Reference 28

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Observation 2c885d70-b583-4839-9c76-d0711018f7f9 · outbound

This paper cites Multiﬁdelity uncertainty propagation via adaptive surrogates in coupled multidisciplinary systems.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Multiﬁdelity uncertainty propagation via adaptive surrogates in coupled multidisciplinary systems

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.229153Z

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Observation 2dc4fc49-aaf8-46a9-8581-3487a920ece7 · outbound

This paper cites A multi-ﬁdelity adaptive sampling method for metamodel-based uncertainty quantiﬁcation of computer simulations.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty A multi-ﬁdelity adaptive sampling method for metamodel-based uncertainty quantiﬁcation of computer simulations

Reference 30

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Observation cec54187-8e0b-41d2-8c9e-be140fe3df65 · outbound

This paper cites Kriging is well-suited to parallelize optimization.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Kriging is well-suited to parallelize optimization

Reference 31

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Unresolved cited work

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Unresolved cited work

Reference 33

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Observation 682c982e-1a9c-4bcb-ab93-3ee5a65b1be3 · outbound

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A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Unresolved cited work

Reference 34

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This paper cites Pattern recognition and machine learning.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Pattern recognition and machine learning

Reference 35

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Observation d65e566c-b4d8-47af-923c-f7d778da94e0 · outbound

This paper cites A self-training approach to cost sensitive uncertainty sampling.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty A self-training approach to cost sensitive uncertainty sampling

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.210214Z

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Observation 6a15cf37-ea4b-4d7a-bb38-63985416f367 · outbound

This paper cites Expected-improvement-based methods for adaptive sampling in multi-objective optimization problems.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Expected-improvement-based methods for adaptive sampling in multi-objective optimization problems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.207417Z

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Observation f24ddad2-3b42-4884-a6b7-5c55ac6326aa · outbound

This paper cites Engineering design via surrogate modelling: a practical guide.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Engineering design via surrogate modelling: a practical guide

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.204271Z

Source-reported events for the cited work

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Observation cff4c717-a30f-48d6-a131-457a1f71c58e · outbound

This paper cites Tuning complex computer codes to data and optimal designs.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Tuning complex computer codes to data and optimal designs

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.314216Z

Source-reported events for the cited work

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Observation dfc1714b-6af6-4e5d-8f67-c0fc2988535c · outbound

This paper cites A statistical method for tuning a computer code to a data base.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty A statistical method for tuning a computer code to a data base

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.201188Z

Source-reported events for the cited work

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Observation f114c3ff-a148-4e25-a4f0-c69c53d86792 · outbound

This paper cites Sequential design and analysis of high-accuracy and low-accuracy computer codes.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Sequential design and analysis of high-accuracy and low-accuracy computer codes

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.198429Z

Source-reported events for the cited work

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Observation 38fc8926-8595-4c64-a684-1ec3561d2c83 · outbound

This paper cites Modeling the steady-state thermodynamic operation point of top-spray ﬂuidized bed processing.

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Modeling the steady-state thermodynamic operation point of top-spray ﬂuidized bed processing

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T15:09:37.195641Z

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.

Pith citing papers

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