Geometric deep learning for statics-aware 3D gridshells

Favilli A., Giorgi D., Laccone F., Malomo L., Cignoni P.

Machine learning  Graph neural network  Geometry processing  Mesh  Gridshell  Statics  Shape 

In the context of architecture, gridshells are three-dimensional frame structures in which loads are entirely born by edges, or beams. Our contribution is to draw the way to a computational method that, given an input gridshell provided by a designer, slightly changes the input to ensure good static performance. The changing is induced by structure node repositioning. If the gridshell is represented as a surface mesh, the problem boils down to finding a proper vertex displacement. The vertex displacement should strike a happy medium between structure rigidity, with load deformation as low as possible, and structure resistance, preventing stress caused breaks. In this report, we inculde a solution to solve this mesh vertex displacement learning problem with a target goal of reducing a physically-based loss function, namely the mean strain energy of a gridshell, by means of a graph neural network. We adopt several geometric input features and discuss their effects on the results.

Source: ISTI Technical Report, ISTI-2022-TR/016, 2022

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