Analytical Benchmark Problems for Multifidelity Optimization Methods
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Abstract (arXiv)
The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Apr 2022.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 0; 2026: 3.
7 papers.
- 2023Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goalcited by 17
- 2024Multi-Fidelity Methods for Optimization: A Surveycited by 6
- 2022Non-Myopic Multifidelity Bayesian Optimizationcited by 2
- 2026Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactorscited by 1
- 2022A Principled Method for the Creation of Synthetic Multi-fidelity Data Setscited by 0
- 2026Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization taskscited by 0
- 2026Transfer Learning in Bayesian Optimization for Aircraft Designcited by 0
- 2026Transfer Learning in Bayesian Optimization for Aircraft Design30 Mar 2026
- 2026Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors14 Mar 2026
- 2026Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks9 Feb 2026
- 2024Multi-Fidelity Methods for Optimization: A Survey15 Feb 2024
- 2023Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal2 Mar 2023
- 2022A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets11 Aug 2022
- 2022Non-Myopic Multifidelity Bayesian Optimization13 Jul 2022