In an era of pervasive digitalization, the growing volume and variety of data streams poses a
new challenge to the efficient running of data-driven optimization algorithms. Targeting …

This work presents a new software, programmed as a Python class, that implements a multi-
objective Bayesian optimization algorithm. The proposed method is able to calculate the …

Bayesian optimization is a metamodel-based global optimization approach that can balance
between exploration and exploitation. It has been widely used to solve single-objective …

In recent years, significant progress has been made in the studies of the spring-driven
autoinjector, leading to an improved understanding of this device and its interactions with …

Inferring arbitrary quantities of interest (QoI) using limited computational or, in realistic
scenarios, financial budgets, is a challenging problem that requires sophisticated strategies …

One of the critical components in industrial gas turbines (IGT) is the turbine blade. The
design of turbine blades needs to consider multiple aspects like aerodynamic efficiency …

A systematic approach to mass-production of graphene and other 2D materials is essential
for current and future technological applications. By combining a sequential statistical …

This work reports the use of a high-flux solar simulator that mimics the solar spectrum and a
cold-wall CVD reactor to demonstrate the feasibility of utilizing a renewable energy resource …

Bayesian optimal design of experiments (BODEs) have been successful in acquiring
information about a quantity of interest (QoI) which depends on a black-box function. BODE …

Multi-objective optimization is an essential component of nearly all engineering design.
However, for industrial applications, the design process typically demands running …