Modeling of multiwavelength metasurfaces relies on adjusting the phase of individual
nanoresonators at several wavelengths. The traditional procedure neglects the near-field …

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is
applicable to a very wide range of engineering applications. To leverage the capability of the …

In this work, the efficient robust global optimization (ERGO) method is revisited with the aim
of enhancing and expanding its existing capabilities. The original objective of ERGO was to …

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is
applicable to a very wide range of engineering applications. To leverage the capability of the …

In this work, robust design optimization (RDO) is treated, motivated by the increasing desire
to account for variability in the design phase. The problem is formulated in a multi-objective …

In this work, optimization-under-uncertainty (OUU) is treated by simultaneously minimizing
the mean of the objective and its variance due to variability of design variables and/or …

Bayesian optimisation (BO) has been widely used to solve problems with expensive function
evaluations. In multi-objective optimisation problems, BO aims to find a set of approximated …

Bayesian optimisation is a popular approach for optimising expensive black-box functions.
The next location to be evaluated is selected via maximising an acquisition function that …

Many real-world multi-objective optimisation problems rely on computationally expensive
function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the …

Abstract Conditional Nonlinear Optimal Perturbation (CNOP) is widely used in atmospheric
and oceanic predictability studies. Solving CNOP is essentially a nonlinear optimization …