Paper Citation Record · LEDGER

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings

As of 15 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2502.00854.

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

pith.paper-citation-record.v1
2502.00854 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:35:41.391607Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

verified exact3
verified fuzzy40
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 448b6646-4101-4ef3-b688-538fbaf9912d · outbound

This paper cites Problem Formulation for Multidisciplinary Optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Problem Formulation for Multidisciplinary Optimization,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.946922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a1c9db3f-e08a-4037-bed3-0796604c7a83 · outbound

This paper cites Multidisciplinary exploration of DRAGON: an ONERA hybrid electric distributed propulsion concept,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Multidisciplinary exploration of DRAGON: an ONERA hybrid electric distributed propulsion concept,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.935540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e19e774-04c9-47cc-bbdf-2926d65b0ee1 · outbound

This paper cites An Efficient Application of Bayesian Optimization to an Industrial MDO Framework for Aircraft Design.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings An Efficient Application of Bayesian Optimization to an Industrial MDO Framework for Aircraft Design

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.923884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f8e00664-5760-47a3-bfe8-b97cf3167596 · outbound

This paper cites Adaptive Modeling Strategy for Constrained Global Optimization with Application to Aerodynamic Wing Design,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Adaptive Modeling Strategy for Constrained Global Optimization with Application to Aerodynamic Wing Design,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.911587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5a461e57-79b8-4e5d-964d-7494a0d1fd85 · outbound

This paper cites Efficient Global Optimization for High-Dimensional ConstrainedProblemsbyUsingtheKrigingModelsCombinedwiththePartialLeastSquaresMethod,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Efficient Global Optimization for High-Dimensional ConstrainedProblemsbyUsingtheKrigingModelsCombinedwiththePartialLeastSquaresMethod,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.899213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 839f4a53-81ed-47f2-8f00-dd40df07bf70 · outbound

This paper cites High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T17:35:41.247305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 272a9fa4-5a08-43ec-9409-be1549c127f4 · outbound

This paper cites Design of a commercial aircraft environment control system using Bayesian optimization techniques.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Design of a commercial aircraft environment control system using Bayesian optimization techniques

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:35:41.474725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 686625f0-8eba-458c-b574-262fb640d1ce · outbound

This paper cites A Tutorial on Bayesian Optimization.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings A Tutorial on Bayesian Optimization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T17:35:41.255467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42f15c39-d75f-482f-8e09-d06483ccb2e1 · outbound

This paper cites Efficient Global Optimization of Expensive Black-Box Functions,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Efficient Global Optimization of Expensive Black-Box Functions,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.879562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 058541ac-a044-4632-aa19-33828e816339 · outbound

This paper cites On Bayesian Methods for Seeking the Extremum,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings On Bayesian Methods for Seeking the Extremum,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.867999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2c43f174-9701-461a-9fc6-c6b4329066ac · outbound

This paper cites Taking the Human Out of the Loop: A Review of Bayesian Optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Taking the Human Out of the Loop: A Review of Bayesian Optimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.856522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 12c823f7-721a-478d-bce8-546e93154993 · outbound

This paper cites Bayesian Optimization with Unknown Constraints,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Bayesian Optimization with Unknown Constraints,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.845459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5d2102ff-fe2b-42ab-9343-d6b664bcbce0 · outbound

This paper cites TREGO: a trust-region framework for efficient global optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings TREGO: a trust-region framework for efficient global optimization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.834637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7ad61d7b-294d-479f-8a12-fb9c3fe6ebf3 · outbound

This paper cites Scalable Global Optimization via Local Bayesian Optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Scalable Global Optimization via Local Bayesian Optimization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.823385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ee55c598-0496-4229-90ca-4e22088d66af · outbound

This paper cites Bayesian Optimization in a Billion Dimensions via Random Embeddings,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Bayesian Optimization in a Billion Dimensions via Random Embeddings,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.812168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1e9a44a5-0069-4afa-b369-fdbfd45aeb88 · outbound

This paper cites Batched High-Dimensional Bayesian Optimization via Structural Kernel Learning,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Batched High-Dimensional Bayesian Optimization via Structural Kernel Learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.800614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5c4f7ad5-af63-440a-aa94-b6552c75c81c · outbound

This paper cites On the Choice of the Low-Dimensional Domain for Global Optimization via Random Embeddings,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings On the Choice of the Low-Dimensional Domain for Global Optimization via Random Embeddings,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.789271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34c4fa1c-673c-4ce2-84ee-727cb1d4f141 · outbound

This paper cites High Dimensional Bayesian Optimisation and Bandits via Additive Models,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings High Dimensional Bayesian Optimisation and Bandits via Additive Models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.778067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d3308dab-3e59-40de-90af-18fc2cd069fa · outbound

This paper cites Scalable Constrained Bayesian Optimization.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Scalable Constrained Bayesian Optimization

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:35:41.446309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cd5ba46f-3a0c-48fd-8d50-991f045cc70d · outbound

This paper cites E., and Williams, C.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings E., and Williams, C

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.765277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 185fb360-db45-41fb-b040-0314fdd0d8eb · outbound

This paper cites A Statistical Approach to Some Basic Mine Valuation Problems on the Witwatersrand,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings A Statistical Approach to Some Basic Mine Valuation Problems on the Witwatersrand,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.754040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 65d532ad-ecc6-4461-9189-9d374fc65a9e · outbound

This paper cites Max-Value Entropy Search for Efficient Bayesian Optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Max-Value Entropy Search for Efficient Bayesian Optimization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.742717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6a3a54d3-d31d-4ec9-b371-95dfecd1106c · outbound

This paper cites aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:35:41.429149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3a814618-c24d-45bd-91b9-eaf2266084da · outbound

This paper cites StructureDiscoveryinNonparametricRegressionthrough Compositional Kernel Search,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings StructureDiscoveryinNonparametricRegressionthrough Compositional Kernel Search,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.730720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 448a565c-b5d9-4767-b992-ea937897e4f4 · outbound

This paper cites Bayesian Data Analysis (Vol. 2),.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Bayesian Data Analysis (Vol. 2),

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.719437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 298be858-42e9-4bf1-97bf-adcee81a1fbe · outbound

This paper cites Optimisation bayésienne sous contraintes et en grande dimension appliquée à la conception avion avant projet,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Optimisation bayésienne sous contraintes et en grande dimension appliquée à la conception avion avant projet,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.708399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0a48d173-6235-44e1-8892-e11b463e584f · outbound

This paper cites OntheStructureofPartialLeastSquaresRegression,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings OntheStructureofPartialLeastSquaresRegression,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.696744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 05a74285-6e7d-419d-af5d-912c2952a358 · outbound

This paper cites Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.685983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa5b9ba1-f8a0-4770-8f84-4f4f5b24327c · outbound

This paper cites A Framework for Bayesian Optimization in Embedded Subspaces,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings A Framework for Bayesian Optimization in Embedded Subspaces,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.673518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1ed888bb-9a01-4dfe-965c-aa0c84a237bb · outbound

This paper cites Active Learning of Linear Embeddings for Gaussian Processes,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Active Learning of Linear Embeddings for Gaussian Processes,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.661367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7d294164-a6e5-427e-8354-8986d896cdf0 · outbound

This paper cites Extensions to the Design Structure Matrix for the Description of Multidisciplinary Design, Analysis, and Optimization Processes,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Extensions to the Design Structure Matrix for the Description of Multidisciplinary Design, Analysis, and Optimization Processes,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.649077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9f1c538d-c765-49c2-a47b-adfef288fec1 · outbound

This paper cites A Python Surrogate Modeling Framework with Derivatives,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings A Python Surrogate Modeling Framework with Derivatives,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.637318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 473f013d-30ae-411f-88d4-f5c33d14b565 · outbound

This paper cites SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.625943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 730d433a-41b9-4c70-8689-88dfeeff881f · outbound

This paper cites Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.614795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c63888b3-3b3d-4255-a075-6261ff926801 · outbound

This paper cites Search biases in constrained evolutionary optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Search biases in constrained evolutionary optimization,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.603508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d91fe4b1-4308-4ff9-95c6-1ea428e8412c · outbound

This paper cites The NLopt nonlinear-optimization package,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings The NLopt nonlinear-optimization package,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.592216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9cd0776b-b199-4f41-bd4f-baa22b508ae8 · outbound

This paper cites SNOPT: An SQP algorithm for large-scale constrained optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings SNOPT: An SQP algorithm for large-scale constrained optimization,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.579343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bb354c01-0ff4-4c11-9752-d000ceaab239 · outbound

This paper cites pyOpt: aPython-basedobject-orientedframeworkfornonlinearconstrained optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings pyOpt: aPython-basedobject-orientedframeworkfornonlinearconstrained optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.568138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4b036e77-0c6d-44d8-82c2-ca5e88e022c4 · outbound

This paper cites CVXOPT: Python software for convex optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings CVXOPT: Python software for convex optimization,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.556812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dc0e9477-d3c7-47de-9928-8405f0b15df5 · outbound

This paper cites Discovering and Exploiting Additive Structure for Bayesian Optimization,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Discovering and Exploiting Additive Structure for Bayesian Optimization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.545714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1fc6e3f7-bbde-4641-8cc8-4677617ce624 · outbound

This paper cites Scikit-learn: Machine Learning in Python,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Scikit-learn: Machine Learning in Python,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.534003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71640dc9-5639-49a2-b616-226140a80005 · outbound

This paper cites Infill Sampling Criteria for Surrogate-Based Optimization with Constraint Handling,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Infill Sampling Criteria for Surrogate-Based Optimization with Constraint Handling,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.522891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a407c885-a5b2-4984-ac09-a328966097db · outbound

This paper cites High dimensional Bayesian optimization assisted by principal component analysis,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings High dimensional Bayesian optimization assisted by principal component analysis,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.511659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c8742a4-fb5c-4f3c-a1c3-19cc8c1e865c · outbound

This paper cites Twofold Adaptive Design Space Reduction for Constrained Bayesian Optimization of Transonic Compressor,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Twofold Adaptive Design Space Reduction for Constrained Bayesian Optimization of Transonic Compressor,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.499397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2dedaf97-c623-42ab-ac4b-bf9291ff860f · outbound

This paper cites Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design,.

High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:35:41.487370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.