Over the past few decades, modeling, simulation, and optimization tools have received
attention for their ability to represent and improve complex systems. The use of …

Attributed to the rising popularity of electric vehicles, the global demand for Li-ion batteries
(LIBs) has been increasing steadily. This creates several potential issues in the raw material …

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible
performance of Machine Learning (ML) algorithms. Several methods have been developed …

A set of constitutive model parameters along with crystallography governs the activation of
deformation mechanisms in crystal plasticity. The constitutive parameters are typically …

Tasks such as analysis, design optimization, and uncertainty quantification can be
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …

Multi-objective optimization problems in many real-world applications are characterized by
computationally or economically expensive objectives, which cannot provide sufficient …

In dealing with the expensive multiobjective optimization problem, some algorithms convert
it into a number of single-objective subproblems for optimization. At each iteration, these …

Aircraft maintenance design aims to identify strategies that render the aircraft reliable for
flight in a cost-efficient manner. These are often conflicting objectives. Moreover, existing …

Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

Real-world supply chain management problems are highly complicated such that their
optimization procedure is computationally expensive due to the extensive dimensions and …