  As demand for computational resources reaches unprecedented levels, research
is expanding into the use of complex material substrates for computing. In this
study, we interface with a model of a hydrodynamic system, under development by
a startup, as a computational reservoir and optimize its properties using an
evolution in materio approach. Input data are encoded as waves applied to our
shallow water reservoir, and the readout wave height is obtained at a fixed
detection point. We optimized the readout times and how inputs are mapped to
the wave amplitude or frequency using an evolutionary search algorithm, with
the objective of maximizing the system's ability to linearly separate
observations in the training data by maximizing the readout matrix determinant.
Applying evolutionary methods to this reservoir system substantially improved
separability on an XNOR task, in comparison to implementations with
hand-selected parameters. We also applied our approach to a regression task and
show that our approach improves out-of-sample accuracy. Results from this study
will inform how we interface with the physical reservoir in future work, and we
will use these methods to continue to optimize other aspects of the physical
implementation of this system as a computational reservoir.
