mlsanityData to 14 Oct 2026

Bayesian optimization as a flexible and efficient design framework for sustainable process systems

arXiv 2401.16373 ↗First posted 29 Jan 2024cs.LG math.OCcited by 15

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Abstract (arXiv)

Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond. In this paper, we provide an overview of recent developments, challenges, and opportunities in BO for design of next-generation process systems. After describing several motivating applications, we discuss how advanced BO methods have been developed to more efficiently tackle important problems in these applications. We conclude the paper with a summary of challenges and opportunities related to improving the quality of the probabilistic model, the choice of internal optimization procedure used to select the next sample point, and the exploitation of problem structure to improve sample efficiency.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 30 of its 54 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

30 of 54 references.

References · 30

  1. 2018A Tutorial on Bayesian Optimizationcited by 744
  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 374
  3. 2023Unexpected Improvements to Expected Improvement for Bayesian Optimizationcited by 149
  4. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 141
  5. 2016Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Roboticscited by 134
  6. 2020Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimizationcited by 92
  7. 2017Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Spacecited by 76
  8. 2022Recent Advances in Bayesian Optimizationcited by 76
  9. 2019Bayesian Optimisation over Multiple Continuous and Categorical Inputscited by 56
  10. 2021Practical and Rigorous Uncertainty Bounds for Gaussian Process Regressioncited by 49
  11. 2021Bayesian Optimization of Function Networkscited by 33
  12. 2022Computer-Aided Multi-Objective Optimization in Small Molecule Discoverycited by 31
18 more
  1. 2016Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangiancited by 29
  2. 2019Mixed-Variable Bayesian Optimizationcited by 29
  3. 2021Bayesian Optimization over Hybrid Spacescited by 23
  4. 2022Multi-Fidelity Cost-Aware Bayesian Optimizationcited by 20
  5. 2021COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive informationcited by 16
  6. 2022Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimizationcited by 16
  7. 2023Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimizationcited by 16
  8. 2022SnAKe: Bayesian Optimization with Pathwise Explorationcited by 14
  9. 2022Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spacescited by 14
  10. 2021Multi-Objective Constrained Optimization for Energy Applications via Tree Ensemblescited by 12
  11. 2022Bayesian optimization with known experimental and design constraints for chemistry applicationscited by 11
  12. 2022Safe Bayesian Optimization using Interior-Point Methods -- Applied to Personalized Insulin Dose Guidancecited by 7
  13. 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
  14. 2023MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learningcited by 6
  15. 2023Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulationscited by 6
  16. 2023No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximationscited by 6
  17. 2023Pareto Optimization to Accelerate Multi-Objective Virtual Screeningcited by 6
  18. 2023Practical Path-based Bayesian Optimizationcited by 2

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Citing papers per year: 2024: 4; 2025: 5; 2026: 6.

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  1. 2024Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximationcited by 8
  2. 2024EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimizationcited by 6
  3. 2025BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertaintycited by 5
  4. 2024Human-Algorithm Collaborative Bayesian Optimization for Engineering Systemscited by 3
  5. 2024System-Aware Neural ODE Processes for Few-Shot Bayesian Optimizationcited by 3
  6. 2025Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A Path to Greener Profits and Smarter Designscited by 3
  7. 2025Efficient optimization of expensive black-box simulators via marginal means, with application to neutrino detector designcited by 2
  8. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  9. 2025Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Designcited by 2
  10. 2026Bayesian Optimization of Partially Known Systems using Hybrid Modelscited by 0
  11. 2026An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functionscited by 0
  12. 2026Meta-learning for sample-efficient Bayesian optimisation of fed-batch processescited by 0
3 more
  1. 2026How Many Initial Points Does Bayesian Optimization Need?cited by 0
  2. 2026Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Modelscited by 0
  3. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0