Geometry-Informed Neural Networks

• URL: http://arxiv.org/abs/2402.14009v3
• Date: Mon, 14 Oct 2024 14:15:05 GMT
• Title: Geometry-Informed Neural Networks
• Authors: Arturs Berzins, Andreas Radler, Eric Volkmann, Sebastian Sanokowski, Sepp Hochreiter, Johannes Brandstetter,
• Abstract summary: We introduce geometry-informed neural networks (GINNs) GINNs are a framework for training shape-generative neural fields without data. We apply GINNs to several validation problems and a realistic 3D engineering design problem.
• Score: 15.27249535281444
• License:
• Abstract: Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learning methods and motivates the exploration of alternative learning strategies. To this end, we introduce geometry-informed neural networks (GINNs) -- a framework for training shape-generative neural fields without data by leveraging user-specified design requirements in the form of objectives and constraints. By adding diversity as an explicit constraint, GINNs avoid mode-collapse and can generate multiple diverse solutions, often required in geometry tasks. Experimentally, we apply GINNs to several validation problems and a realistic 3D engineering design problem, showing control over geometrical and topological properties, such as surface smoothness or the number of holes. These results demonstrate the potential of training shape-generative models without data, paving the way for new generative design approaches without large datasets.

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