Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Paulson, Joel A., Tsay, Calvin
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Global Optimization of Gaussian Process Acquisition Functions Using a Piecewise-Linear Kernel Approximation
by: Xie, Yilin, et al.
Published: (2024)

An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
by: Tang, Wei-Ting, et al.
Published: (2026)

BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertainty
by: Kudva, Akshay, et al.
Published: (2025)

Relaxation-Informed Training of Neural Network Surrogate Models
by: Tsay, Calvin
Published: (2026)

Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
by: Langdon, Becky, et al.
Published: (2026)

BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification
by: Lu, Yen-An, et al.
Published: (2024)

Training Neural ODEs Using Fully Discretized Simultaneous Optimization
by: Shapovalova, Mariia, et al.
Published: (2025)

Global optimization of graph acquisition functions for neural architecture search
by: Xie, Yilin, et al.
Published: (2025)

EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
by: Cheon, Mujin, et al.
Published: (2024)

P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
by: Kronqvist, Jan, et al.
Published: (2022)

NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information
by: Tang, Wei-Ting, et al.
Published: (2025)

When Deep Learning Meets Polyhedral Theory: A Survey
by: Huchette, Joey, et al.
Published: (2023)

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
by: Saves, Paul, et al.
Published: (2025)

BoGrape: Bayesian optimization over graphs with shortest-path encoded
by: Xie, Yilin, et al.
Published: (2025)

Inverse classification with logistic and softmax classifiers: efficient optimization
by: Carreira-Perpiñán, Miguel Á., et al.
Published: (2023)

Distributed optimization: designed for federated learning
by: Guo, Wenyou, et al.
Published: (2025)

SafEDMD: A Koopman-based data-driven controller design framework for nonlinear dynamical systems
by: Strässer, Robin, et al.
Published: (2024)

Scaling Mixed-Integer Programming for Certification of Neural Network Controllers Using Bounds Tightening
by: Sosnin, Philip, et al.
Published: (2024)

Bayesian optimization for stable properties amid processing fluctuations in sputter deposition
by: Shrivastava, Ankit, et al.
Published: (2024)

qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
by: Renganathan, Ashwin, et al.
Published: (2023)

Towards safe Bayesian optimization with Wiener kernel regression
by: Molodchyk, Oleksii, et al.
Published: (2024)

PACSBO: Probably approximately correct safe Bayesian optimization
by: Tokmak, Abdullah, et al.
Published: (2024)

TS-RSR: A provably efficient approach for batch Bayesian Optimization
by: Ren, Zhaolin, et al.
Published: (2024)

Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning
by: Kchaou, Mahdi, et al.
Published: (2025)

High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
by: Saves, Paul, et al.
Published: (2023)

A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
by: Dagréou, Mathieu, et al.
Published: (2022)

Gearing Gaussian process modeling and sequential design towards stochastic simulators
by: Binois, Mickael, et al.
Published: (2024)

Safe Bayesian optimization across noise models via scenario programming
by: Tokmak, Abdullah, et al.
Published: (2025)

Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
by: Wu, Long, et al.
Published: (2024)

PolyFormer: learning efficient reformulations for scalable optimization under complex physical constraints
by: Wen, Yilin, et al.
Published: (2026)

General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization
by: Ahn, Kwangjun, et al.
Published: (2024)

Derivative-free tree optimization for complex systems
by: Wei, Ye, et al.
Published: (2024)

Surrogate-assisted multi-objective design of complex multibody systems
by: Amakor, Augustina C., et al.
Published: (2024)

Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
by: Brevault, Loic, et al.
Published: (2023)

A block-coordinate descent framework for non-convex composite optimization. Application to sparse precision matrix estimation
by: Lauga, Guillaume
Published: (2026)

Estimation Network Design framework for efficient distributed optimization
by: Bianchi, Mattia, et al.
Published: (2024)

Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design
by: Cordelier, Oihan, et al.
Published: (2026)

A Quantile Neural Network Framework for Two-stage Stochastic Optimization
by: Alcántara, Antonio, et al.
Published: (2024)

Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead information
by: Zheng, Taicheng, et al.
Published: (2025)

A stochastic smoothing framework for nonconvex-nonconcave min-sum-max problems with applications to Wasserstein distributionally robust optimization
by: Liu, Wei, et al.
Published: (2025)