Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise
for large search spaces, even when automated. This poses an optimization task on a …

Optimizing expensive to evaluate black-box functions over an input space consisting of all
permutations of d objects is an important problem with many real-world applications. For …

One method to solve expensive black-box optimization problems is to use a surrogate model
that approximates the objective based on previous observed evaluations. The surrogate …

With the advent of electric vertical-takeoff-and-landing (eVTOL) vehicles, it becomes
imperative to assess their impact on transportation network efficiency. Hence, this paper …

Deep learning methods have been widely studied in system modeling due to their strong
abilities in feature representation and function fitting. However, most deep learning models …

Device sizing is a challenging problem for analog circuit design. Traditional methods
depend on domain knowledge and intensive simulations to search for feasible parameters …

This paper reviews the state-of-the-art model-based adaptive sampling approaches for
single-objective black-box optimization (BBO). While BBO literature includes various …

Bio-inspired hierarchical discontinuous fibrous composite materials are investigated with the
aim of achieving enhanced pseudo-ductility and elevated toughness. A novel methodology …

Quality control is key in the advanced manufacturing of complex parts. Modern precision
manufacturing must identify and exclude parts with visual imperfections (eg, scratches …

This paper integrates manifold learning techniques within a Gaussian process upper
confidence bound algorithm to optimize an objective function on a manifold. Our approach is …