Geometry encoding for numerical simulations

Amir Maleki, Jan Heyse, Rishikesh Ranade, Haiyang He, Priya Kasimbeg, Jay Pathak

Keywords: Geometry encoding, Implicit representation, Neural network

TL;DR: We present a notion of geometry encoding suitable for machine learning-based numerical simulations

Abstract: 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.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2104.07792/code)