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differentiable-geometry

topic1 events
papersSEP 10 04:00 UTC

Neural parametric geometry representation proposed for thin-shell shape optimisation

Researchers have introduced a neural-network-based parametric geometry representation designed for thin-shell structures. The method offers a differentiable surface description that can feed gradient-based shape optimisation workflows, where flexible geometric modelling is a key requirement. The paper is available on arXiv in the machine learning category as an updated version.

neural-networks/shape-optimisation/geometry/research