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Found 5 papers, 0 papers with code
Machine learning for industrial sensing and control: A survey and practical perspective
Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes
This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes.
Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes.
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems
But experiments may be less expensive than BO methods assume: In some simulation models, we may be able to conduct multiple thousands of experiments in a few hours, and the computational burden of BO is no longer negligible compared to experimentation time.
