Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Bayesian optimization (BO) has been widely applied to several modern science and
engineering applications such as machine learning, neural networks, robotics, aerospace …

Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

This article investigates deep neural networks (DNNs)-based lung nodule classification with
hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally …

Overfitting describes the phenomenon where a highly predictive model on the training data
generalizes poorly to future observations. It is a common concern when applying machine …

Learning decompositions of expensive-to-evaluate black-box functions promises to scale
Bayesian optimisation (BO) to high-dimensional problems. However, the success of these …

We propose a novel method for gradient-based optimization of black-box simulators using
differentiable local surrogate models. In fields such as physics and engineering, many …

Despite the recent success of Bayesian optimization (BO) in a variety of applications where
sample efficiency is imperative, its performance may be seriously compromised in settings …

Bayesian Optimization (BO) is a technique for sample-efficient black-box optimization that
employs probabilistic models to identify promising input locations for evaluation. When …