  We present a notion of geometry encoding suitable for machine learning-based
numerical simulation. In particular, we delineate how this notion of encoding
is different than other encoding algorithms commonly used in other disciplines
such as computer vision and computer graphics. We also present a model
comprised of multiple neural networks including a processor, a compressor and
an evaluator.These parts each satisfy a particular requirement of our encoding.
We compare our encoding model with the analogous models in the literature
