Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even ...

Achieving high efficiency, long operational lifetime, and excellent color purity is essential for organic light-emitting diode (OLED) materials used in next-generation display and lighting ...

In this research work authors have experimentally validated a blend of Machine Learning and Nonlinear Model Predictive Control (NMPC) framework designed to track the temperature profile in a Batch ...

Conventional vehicle suspension systems, often relying on integer-order models with fixed damping coefficients, struggle to deliver optimal performance across diverse an ...

Embedding Plant Constraints into Optimization Models Optimization in power plants isn’t just about maximizing efficiency—it must also respect operational, safety, and regulatory constraints.

The framework combines a lightweight model for initial image assessment and a depth-wise model for selective processing of uncertain cases. Bayesian optimization is employed to determine the optimal ...

The combination of Bayesian optimization for data selection, singular value decomposition (SVD) for full-spectrum fitting, and XGBoost for predictive modeling provides a powerful and generalizable ...

During the design of electrical machines, multiple performance objectives need to be considered. Although stochastic optimization algorithms are extensively employed for this purpose, a primary ...

Because sensor fusion is based on modeling, various forms of Bayesian inference are key to performance optimization. Bayesian inference uses probability models to make inferences about probability ...

This Unity asset integrates Bayesian Optimization (BO) (based on botorch.org) into your projects, enabling the optimization of design parameters to maximize or minimize objective values. It utilizes a ...