Paper Citation Record · LEDGER

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2501.14012.

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

pith.paper-citation-record.v1
2501.14012 v3

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:34:02.033298Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:54:35.748216Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

2
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

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This paper cites an unresolved cited work.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Unresolved cited work

Reference 1

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Observation cdcb57ab-ad1b-4da0-93b7-f5effa4d5f55 · outbound

This paper cites Recent advances in surrogate-based optimization,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Recent advances in surrogate-based optimization,

Reference 2

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This paper cites Advances in surrogate based modeling, feasibility analysis, and optimization: A review,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Advances in surrogate based modeling, feasibility analysis, and optimization: A review,

Reference 3

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This paper cites Surrogate models in evolutionary single-objective optimization: A new taxonomy and experimental study,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Surrogate models in evolutionary single-objective optimization: A new taxonomy and experimental study,

Reference 4

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This paper cites Convolutional neural network surrogate-assisted GOMEA,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Convolutional neural network surrogate-assisted GOMEA,

Reference 5

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This paper cites A constrained competitive swarm optimizer with an svm-based surrogate model for feature selection,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks A constrained competitive swarm optimizer with an svm-based surrogate model for feature selection,

Reference 6

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This paper cites Adaptive bayesian support vector regression model for structural reliability analysis,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Adaptive bayesian support vector regression model for structural reliability analysis,

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This paper cites A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems,

Reference 8

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This paper cites Random forests for global sensitivity analysis: A selective review,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Random forests for global sensitivity analysis: A selective review,

Reference 9

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This paper cites Gaussian process surrogate model with composite kernel learning for engineering design,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Gaussian process surrogate model with composite kernel learning for engineering design,

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This paper cites Deep gaussian process enabled surrogate models for aerodynamic flows,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Deep gaussian process enabled surrogate models for aerodynamic flows,

Reference 11

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Unresolved cited work

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This paper cites Transfer learning of surrogate models via domain affine transformation,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Transfer learning of surrogate models via domain affine transformation,

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This paper cites Learn on source, refine on target: A model transfer learning framework with random forests,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Learn on source, refine on target: A model transfer learning framework with random forests,

Reference 14

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This paper cites Transfer learning in classification based on manifolc. models and its relation to tangent metric learning,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Transfer learning in classification based on manifolc. models and its relation to tangent metric learning,

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This paper cites Improved versions of learning vector quantization,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Improved versions of learning vector quantization,

Reference 16

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This paper cites Aspects in classification learning – review of recent developments in learning vector quantization,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Aspects in classification learning – review of recent developments in learning vector quantization,

Reference 17

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This paper cites Pose-dependent tool tip dynamics prediction using transfer learning,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Pose-dependent tool tip dynamics prediction using transfer learning,

Reference 18

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This paper cites Affine transfor- mations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem,.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Affine transfor- mations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem,

Reference 19

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Evolution strategies,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks The CMA evolution strategy: A comparing review,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks The CMA Evolution Strategy: A Tutorial

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem,

Reference 23

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Towards learning universal hyperparameter optimizers with transformers,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Surrogate-assisted multi-objective optimization via genetic programming based symbolic regression,

Reference 26

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks COCO: a platform for comparing continuous optimizers in a black-box setting,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Revisiting lambert’s problem,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Mars science laboratory launch-arrival space study: a pork chop plot analysis,

Reference 29

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks On the nature of Earth-Mars porkchop plots,

Reference 30

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Unresolved cited work

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Real-world optimization benchmark from vehicle dynamics: Specification of problems in 2d and methodology for transferring (meta-)optimized algorithm parameters,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Single- and multi-objective game-benchmark for evolutionary algorithms,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks A pragmatic view of accuracy measurement in forecasting,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Iohexperimenter: Benchmarking platform for iterative optimization heuristics,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Scikit-learn: Machine learning in Python,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks CMA-ES/pycma on Github,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks SMAC3: A versatile bayesian optimization package for hyperparameter optimization,

Reference 38

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Available: https://doi.org/10.1162/evco a 00342

Reference 39

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks poliastro/poliastro: poliastro 0.17.0 (scipy us ’22 edition),

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Transfer learning of surrogate models via domain affine transformation across synthetic and real- world benchmarks: Supplementary material,

Reference 41

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Input warping for Bayesian optimization of non-stationary functions,

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Transfer learning with affine model transformation,

Reference 43

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This paper cites Available: https://www.worldcat.org/oclc/61285753.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Available: https://www.worldcat.org/oclc/61285753

Reference 2006

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Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Available: http://papers.nips.cc/paper files/paper/2022/ hash/cf6501108fced72ee5c47e2151c4e153-Abstract-Conference.html

Reference 2022

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This paper cites Available: https://doi.org/10.1109/TEVC.2022.3197427.

Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks Available: https://doi.org/10.1109/TEVC.2022.3197427

Reference 2024

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Pith citing papers

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Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations cites this paper.

Transfer Learning of Surrogate Models: Integrating Domain Warping and Affine Transformations Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks

Reference 24

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