Geometry encoding for numerical simulations
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Abstract (arXiv)
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
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 15 Apr 2021.
Builds on
19 of 26 references.
References · 19
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7 more
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.