# Analytical Benchmark Problems for Multifidelity Optimization Methods

arXiv: https://arxiv.org/abs/2204.07867 · HTML: https://mlsanity.com/paper/2204.07867/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2022.

## Built on by (most cited first, 7 of 7)

- [Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal](https://mlsanity.com/paper/2303.01560/) (2023)
- [Multi-Fidelity Methods for Optimization: A Survey](https://mlsanity.com/paper/2402.09638/) (2024)
- [Non-Myopic Multifidelity Bayesian Optimization](https://mlsanity.com/paper/2207.06325/) (2022)
- [Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors](https://mlsanity.com/paper/2603.14143/) (2026)
- [A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets](https://mlsanity.com/paper/2208.05667/) (2022)
- [Weighted Wasserstein Barycenter of Gaussian Processes for exotic Bayesian Optimization tasks](https://mlsanity.com/paper/2602.09181/) (2026)
- [Transfer Learning in Bayesian Optimization for Aircraft Design](https://mlsanity.com/paper/2603.28999/) (2026)

## 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.