Constrained Design of Experiments forData-Driven Models

The quality of data-driven models depends significantly on the data distribution
in the input space. In this work, design of experiments (DoE) methods
for constrained input spaces are discussed. An approach based on an Latin
hypercube (LH) design is introduced to deal with strongly constrained input
spaces. For the unconstrained case, where the input space is a hypercube,
different design of experiments methods have been developed. The dominating
state-of-the-art methods are Sobol sequences and Latin hypercubes. Instead
of optimizing complete LH designs, the proposed strategy is to incrementally
construct an LH design. Every new sample is selected by a distance-based
metric. The presented method is then applied in two test cases and compared
to a method based on a Sobol sequence. Here, an initial design is created by a
Sobol sequence and every sample is removed that violates the constraints. The
design qualities are measured by the resulting model accuracies of data-driven
models. A function generator is applied to create synthetic data sets to train
and evaluate local linear model networks.