Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:54:42.781111Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2505.17279.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:54:42.781111Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-13T23:23:02.433224Z
A source-named dated measurement, never combined with another source.
Source: cited_works
73 of 73 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 1456e335-e751-460f-b9f7-99c26691b291 · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Le Clainche, E
Reference 1
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 5
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Reference 13
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 15
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 16
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Observation 1a394cde-f14d-4cdd-a962-b5ad60818b99 · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 18
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Reference 20
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Observation 1af2885a-5142-4e8b-b086-d111869c5fc2 · outbound
Reference 21
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Matar, Analysis of loss mechanisms inside Organic Rankine Cycle turbines and multi-fidelity optimization with LES and RANS coupling, Ph.D
Reference 22
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Cost-effective multi-fidelity strategy for the optimization of high-Reynolds number turbine flows guided by LES
Reference 23
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Reference 24
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Reference 25
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Observation 0b131ffb-a19a-4487-8e49-7eb5eeb86ed6 · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 26
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 28
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Reference 29
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Multi-Fidelity Methods for Optimization: A Survey
Reference 30
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Kennedy, Predicting the output from a complex computer code when fast approximations are available, Biometrika 87 (1) (2000) 1–13
Reference 31
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Reference 32
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Observation 9b29aac8-2d7f-4807-bb2e-c4bb587d9d9f · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 33
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 34
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Deep Gaussian Processes for Multi-fidelity Modeling
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 36
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Observation f5b475f0-f493-431d-af92-26fbca628b4b · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 38
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 40
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 41
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 42
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 43
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 44
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 45
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Active subspace methods in theory and practice: applications to kriging surfaces
Reference 46
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Cinquegrana, E
Reference 47
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 48
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Reference 49
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 50
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Van Langenhove, D
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Reference 52
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Reference 53
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 54
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Reference 55
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Reference 57
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Reference 58
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Reference 59
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 62
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
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Reference 64
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Reference 65
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 66
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Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 67
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Observation 9b5f746c-0867-4132-a670-27c58d2ad18c · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Unresolved cited work
Reference 68
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Observation 5fef9e7e-c0ba-4dcf-9c93-970f8418eb17 · outbound
Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance Adam: A Method for Stochastic Optimization
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Observation fb2c9fa8-cee8-4d04-b6ed-863bc307cc7d · outbound
Reference 70
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Observation 03c5f80b-4f2d-401a-87c5-157b8d8bc6ff · outbound
Reference 71
Source-reported events for the cited work
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Reference 72
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Observation 61cbcbdd-20e5-4218-994f-cd45ed350961 · outbound
Reference 73
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Optimization-Embedded Active Multi-Fidelity Surrogate Learning for Multi-Condition Airfoil Shape Optimization Bayesian and non-Bayesian multi-fidelity surrogate models for multi-objective aerodynamic optimization under extreme cost imbalance
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