  A common workflow for many engineering design problems requires the
evaluation of the design system to be investigated under a range of conditions.
These conditions usually involve a combination of several parameters. To
perform a complete evaluation of a single candidate configuration, it may be
necessary to perform hundreds to thousands of simulations. This can be
computationally very expensive, particularly if several configurations need to
be evaluated, as in the case of the mathematical optimization of a design
problem. Although the simulations are extremely complex, generally, there is a
high degree of redundancy in them, as many of the cases vary only slightly from
one another. This redundancy can be exploited by omitting some simulations that
are uninformative, thereby reducing the number of simulations required to
obtain a reasonable approximation of the complete system. The decision of which
simulations are useful is made through the use of machine learning techniques,
which allow us to estimate the results of "yet-to-be-performed" simulations
from the ones that are already performed. In this study, we present the results
of one such technique, namely active learning, to provide an approximate result
of an entire offshore riser design simulation portfolio from a subset that is
80% smaller than the original one. These results are expected to facilitate a
significant speed-up in the offshore riser design.
