This article presents a novel heuristic for constrained optimization of computationally
expensive random simulation models. One output is selected as objective to be minimized …

Metamodels are commonly used to approximate and analyze simulation models. However,
in cases where the simulation output variances are non-zero and not constant, many of the …

The purpose of this study is to revaluate the changing spatial and temporal trends of
precipitation in Turkey. Turkey is located in one of the regions at greatest risk from the …

This chapter presents an overview of the design and analysis of computational experiments
with optimization algorithms. It covers classic designs and their corresponding (meta) …

Kriging metamodels (also called Gaussian process or spatial correlation models)
approximate the Input/Output functions implied by the underlying simulation models. Such …

This contribution presents an overview of sensitivity analysis of simulation models, including
the estimation of gradients. It covers classic designs and their corresponding (meta) models; …

In the application of kriging model in the field of simulation, the parameters of the model are
likely to be estimated from the simulated data. This introduces parameter estimation …

In the application of kriging model in the field of simulation, the parameters of the model are
likely to be estimated from the simulated data. This introduces parameter estimation …

Numerical noise is an unavoidable by-product of Computational Fluid Dynamics (CFD)
simulations, which bring challenges to optimizations. In the former work, we have proposed …

This thesis proposes three pieces of work on the Gaussian process (GP) model and GP-
based Bayesian optimization (BO) for stochastic simulations. Firstly, we propose a multi …