Bayesian Optimization in Bioprocess Engineering—Where Do We Stand Today?

Biotechnology and Bioengineering, Год журнала: 2025, Номер unknown

Опубликована: Март 5, 2025

Bayesian optimization is a stochastic, global black-box algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing exploration and exploitation. Although Design of Experiments has traditionally been preferred method for optimizing bioprocesses, AI-driven tools have recently drawn increasing attention within bioprocess engineering. This review presents principles methodologies focuses on its application various stages engineering in upstream downstream processing.

Язык: Английский

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XAI-BO: an architecture using Grad-CAM technique to evaluate Bayesian optimization algorithms on deep learning models

Journal of Information and Telecommunication, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Янв. 7, 2025

Язык: Английский

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Application research of Bayesian optimization algorithm in DHCPv6 stateful allocation

Опубликована: Янв. 9, 2025

This paper studies the application of Bayesian optimization algorithm in DHCPv6 stateful allocation, addressing efficiency issues traditional allocation strategies high-load scenarios. By constructing a simulation platform, simulating different network load environments, and validating effects real network, experimental results show that significantly improves response speed address success rates, particularly excelling under conditions. research provides an important reference for further networks verifies practical value algorithm.

Язык: Английский

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Accelerated experimental design using a human-AI teaming framework

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113138 - 113138

Опубликована: Март 1, 2025

Язык: Английский

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