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Found 6 papers, 0 papers with code
Multi-objective hyperparameter optimization with performance uncertainty
The performance of any Machine Learning (ML) algorithm is impacted by the choice of its hyperparameters.
Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding
Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance, and fatigue resistance.
Measuring Wind Turbine Health Using Drifting Concepts
In this paper, we propose two new approaches for the analysis of wind turbine health.
Multi-objective simulation optimization of the adhesive bonding process of materials
Automotive companies are increasingly looking for ways to make their products lighter, using novel materials and novel bonding processes to join these materials together.
A survey on multi-objective hyperparameter optimization algorithms for Machine Learning
Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms.
Online learning of windmill time series using Long Short-term Cognitive Networks
Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling.
