Neural parametric representations for thin-shell shape optimisation

• URL: http://arxiv.org/abs/2604.06612v1
• Date: Wed, 08 Apr 2026 02:45:39 GMT
• Title: Neural parametric representations for thin-shell shape optimisation
• Abstract summary: We propose a neural parametric representation (NRep) for the shell mid-surface based on a neural network with periodic activation functions.<n>A structural compliance optimisation problem is posed to optimise the shape of a thin-shell parameterised by the NRep.<n>The resulting shape optimisation problem is solved using a gradient-based optimisation algorithm.
• Score: 3.8860840464742665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shape optimisation of thin-shell structures requires a flexible, differentiable geometric representation suitable for gradient-based optimisation. We propose a neural parametric representation (NRep) for the shell mid-surface based on a neural network with periodic activation functions. The NRep is defined using a multi-layer perceptron (MLP), which maps the parametric coordinates of mid-surface vertices to their physical coordinates. A structural compliance optimisation problem is posed to optimise the shape of a thin-shell parameterised by the NRep subject to a volume constraint, with the network parameters as design variables. The resulting shape optimisation problem is solved using a gradient-based optimisation algorithm. Benchmark examples with classical solutions demonstrate the effectiveness of the proposed NRep. The approach exhibits potential for complex lattice-skin structures, owing to the compact and expressive geometry representation afforded by the NRep.

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