Experimental designs and modeling are very important in chemometrics and chemical
engineering. There are many kinds of experimental designs, which include the fractional …

The combination of user-centric network densification and distributed massive multiple-input
multiple-output (MIMO) operation has recently brought along a new paradigm in the wireless …

A data-driven model is compared to classical equation-driven approaches to investigate its
ability to predict quantity of interest and their uncertainty when studying airfoil aerodynamics …

Space-filling designs based on orthogonal arrays are attractive for computer experiments for
they can be easily generated with desirable low-dimensional stratification properties …

Fractional factorial designs are widely used in various scientific investigations and industrial
applications. Level permutation of factors could alter their geometrical structures and …

Subsampling or subdata selection is a useful approach in large-scale statistical learning.
Most existing studies focus on model-based subsampling methods which significantly …

In this article, we propose a hybrid approach that combines machine learning and
experimental design to efficiently and accurately predict the monostatic radar cross section …

Probabilistic power system analysis tools like probabilistic power flow (PPF) help system
operators in planning and security studies with multiple uncertainties. Sample-based …

The small-scale linear information in galaxy samples typically lost during nonlinear growth
can be restored to a certain level by the density field reconstruction, which has been …

The threshold accepting (TA) algorithm, a stochastic heuristic technique, has been proposed
as an effective method for constructing uniform designs (UDs). However, some flaws of this …