Cold bent glass is a promising and cost-efficient method for realizing doubly curved glass fa\c{c}ades. They are produced by attaching planar glass sheets to curved frames and require keeping the occurring stress within safe limits. However, it is very challenging to navigate the design space of cold bent glass panels due to the fragility of the material, which impedes the form-finding for practically feasible and aesthetically pleasing cold bent glass fa\c{c}ades. We propose an interactive, data-driven approach for designing cold bent glass fa\c{c}ades that can be seamlessly integrated into a typical architectural design pipeline. Our method allows non-expert users to interactively edit a parametric surface while providing real-time feedback on the deformed shape and maximum stress of cold bent glass panels. Designs are automatically refined to minimize several fairness criteria while maximal stresses are kept within glass limits. We achieve interactive frame rates by using a differentiable Mixture Density Network trained from more than a million simulations. Given a curved boundary, our regression model is capable of handling multistable configurations and accurately predicting the equilibrium shape of the panel and its corresponding maximal stress. We show predictions are highly accurate and validate our results with a physical realization of a cold bent glass surface.

中文翻译：

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冷弯玻璃幕墙的计算设计

冷弯玻璃是一种实现双曲面玻璃幕墙的有前途且经济高效的方法。它们是通过将平面玻璃板连接到弯曲的框架而生产的，并且需要将发生的应力保持在安全范围内。然而，由于材料的脆弱性，在冷弯玻璃面板的设计空间中导航非常具有挑战性，这阻碍了实际可行且美观的冷弯玻璃幕墙的找形。我们提出了一种交互式、数据驱动的方法来设计冷弯玻璃幕墙，可以无缝集成到典型的建筑设计管道中。我们的方法允许非专家用户交互式编辑参数化表面，同时提供有关冷弯玻璃面板变形形状和最大应力的实时反馈。设计会自动优化以最小化几个公平标准，同时将最大应力保持在玻璃限度内。我们通过使用从超过一百万次模拟中训练的可微混合密度网络来实现交互式帧速率。给定弯曲边界，我们的回归模型能够处理多稳态配置并准确预测面板的平衡形状及其相应的最大应力。我们表明预测是高度准确的，并通过冷弯玻璃表面的物理实现来验证我们的结果。我们的回归模型能够处理多稳态配置并准确预测面板的平衡形状及其相应的最大应力。我们表明预测是高度准确的，并通过冷弯玻璃表面的物理实现来验证我们的结果。我们的回归模型能够处理多稳态配置并准确预测面板的平衡形状及其相应的最大应力。我们表明预测是高度准确的，并通过冷弯玻璃表面的物理实现来验证我们的结果。