  In complex simulation environments, certain parameter space regions may
result in non-convergent or unphysical outcomes. All parameters can therefore
be labeled with a binary class describing whether or not they lead to valid
results. In general, it can be very difficult to determine feasible parameter
regions, especially without previous knowledge. We propose a novel algorithm to
explore such an unknown parameter space and improve its feasibility
classification in an iterative way. Moreover, we include an additional
optimization target in the algorithm to guide the exploration towards regions
of interest and to improve the classification therein. In our method we make
use of well-established concepts from the field of machine learning like kernel
support vector machines and kernel ridge regression. From a comparison with a
Kriging-based exploration approach based on recently published results we can
show the advantages of our algorithm in a binary feasibility classification
scenario with a discrete feasibility constraint violation. In this context, we
also propose an improvement of the Kriging-based exploration approach. We apply
our novel method to a fully realistic, industrially relevant chemical process
simulation to demonstrate its practical usability and find a comparably good
approximation of the data space topology from relatively few data points.
