Neural parametric representations for thin-shell shape optimisation
Shape optimisation of thin-shell structures requires a flexible, differentiable geometric representation suitable for gradient-based optimisation. We propose a neural parametric geometry representation (NRep) for shells 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 gradie...
| Authors | Xiao Xiao, Fehmi Cirak |
|---|---|
| Primary category | math.NA |
| Published | 8 April 2026 |
| Last updated at source | 9 September 2026 |
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