  Magnonic systems have been a major area of research interest due to their
potential benefits in speed and lower power consumption compared to traditional
computing. One particular area that they may be of advantage is as Physical
Reservoir Computers in machine learning models. In this work, we build on an
established design for using an Auto-Oscillation Ring as a reservoir computer
by introducing a simple neural network midstream and introduce an additional
design using a spin wave guide with a scattering regime for processing data
with different types of inputs. We simulate these designs on the new micro
magnetic simulation software, Magnum.np, and show that the designs are capable
of performing on various real world data sets comparably or better than
traditional dense neural networks.
