mlsanityData to 7 Oct 2026

Multi-fidelity Bayesian Optimization: A Review

arXiv 2311.13050 ↗First posted 21 Nov 2023cs.CE cs.LG math.OCcited by 12

Not on the map. Why?

Abstract (arXiv)

Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization (BO), MF BO has found a niche in solving expensive engineering design optimization problems, thanks to its advantages in incorporating physical and mathematical understandings of the problems, saving resources, addressing exploitation-exploration trade-off, considering uncertainty, and processing parallel computing. The increasing number of works dedicated to MF BO suggests the need for a comprehensive review of this advanced optimization technique. In this paper, we survey recent developments of two essential ingredients of MF BO: Gaussian process (GP) based MF surrogates and acquisition functions. We first categorize the existing MF modeling methods and MFO strategies to locate MF BO in a large family of surrogate-based optimization and MFO algorithms. We then exploit the common properties shared between the methods from each ingredient of MF BO to describe important GP-based MF surrogate models and review various acquisition functions. By doing so, we expect to provide a structured understanding of MF BO. Finally, we attempt to reveal important aspects that require further research for applications of MF BO in solving intricate yet important design optimization problems, including constrained optimization, high-dimensional optimization, optimization under uncertainty, and multi-objective optimization.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 18 of its 174 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

18 of 174 references.

References · 18

  1. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  2. 2017A Tutorial on Thompson Samplingcited by 420
  3. 2013Learning to Optimize Via Posterior Samplingcited by 253
  4. 2013Bayesian Optimization in a Billion Dimensions via Random Embeddingscited by 178
  5. 2017Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processescited by 65
  6. 2019Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisationcited by 47
  7. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  8. 2020Overview of Gaussian process based multi-fidelity techniques with variable relationship between fidelitiescited by 24
  9. 2021Latent Map Gaussian Processes for Mixed Variable Metamodelingcited by 23
  10. 2019Bayesian Optimization using Deep Gaussian Processescited by 22
  11. 2020Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processescited by 16
  12. 2020Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimizationcited by 15
6 more
  1. 2020Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloyscited by 14
  2. 2020Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Designcited by 11
  3. 2021A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisionscited by 6
  4. 2019Bi-fidelity Stochastic Gradient Descent for Structural Optimization under Uncertaintycited by 5
  5. 2019mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Locationcited by 3
  6. 2021Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategycited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 3.

12 papers.

  1. 2024Epsilon-Greedy Thompson Sampling to Bayesian Optimizationcited by 8
  2. 2024Automated design of nonreciprocal thermal emitters via Bayesian optimizationcited by 3
  3. 2024Multi-fidelity Machine Learning for Uncertainty Quantification and Optimizationcited by 3
  4. 2025Sampling from Gaussian Processes: A Tutorial and Applications in Global Sensitivity Analysis and Optimizationcited by 3
  5. 2025Bayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic Modelscited by 1
  6. 2024Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategiescited by 0
  7. 2025Bayesian Neural Network Surrogates for Bayesian Optimization of Carbon Capture and Storage Operationscited by 0
  8. 2025A Constrained Multi-Fidelity Bayesian Optimization Methodcited by 0
  9. 2025Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systemscited by 0
  10. 2026MAST: A Multi-fidelity Augmented Surrogate model via Spatial Trust-weightingcited by 0
  11. 2026Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regressioncited by 0
  12. 2026MF-SCBO : Multi-fidelity Scalable Constrained Bayesian Optimizationcited by 0