  Wave energy converters (WECs) are a promising candidate for meeting the
increasing energy demands of today's society. It is known that the sizing and
power take-off (PTO) control of WEC devices have a major impact on their
performance. In addition, to improve power generation, WECs must be optimally
deployed within a farm. While such individual aspects have been investigated
for various WECs, potential improvements may be attained by leveraging an
integrated, system-level design approach that considers all of these aspects.
However, the computational complexity of estimating the hydrodynamic
interaction effects significantly increases for large numbers of WECs. In this
article, we undertake this challenge by developing data-driven surrogate models
using artificial neural networks and the principles of many-body expansion. The
effectiveness of this approach is demonstrated by solving a concurrent plant
(i.e., sizing), control (i.e., PTO parameters), and layout optimization of
heaving cylinder WEC devices. WEC dynamics were modeled in the frequency
domain, subject to probabilistic incident waves with farms of $3$, $5$, $7$,
and $10$ WECs. The results indicate promising directions toward a practical
framework for array design investigations with more tractable computational
demands.
