Across fields of science, researchers have increasingly focused on designing soft devices that can shape-morph to achieve functionality. However, identifying a rest shape that leads to a target 3D shape upon actuation is a non-trivial task that involves inverse design capabilities. In this study, a simple and efficient platform is presented to design pre-programmed 3D shapes starting from 2D planar composite membranes. By training neural networks with a small set of finite element simulations, the authors are able to obtain both the optimal design for a pixelated 2D elastomeric membrane and the inflation pressure required for it to morph into a target shape. The proposed method has potential to be employed at multiple scales and for different applications. As an example, it is shown how these inversely designed membranes can be used for mechanotherapy applications, by stimulating certain areas while avoiding prescribed locations.

中文翻译：

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通过机器学习逆向设计充气软膜

在各个科学领域，研究人员越来越关注设计可以变形以实现功能的软设备。然而，识别在驱动时导致目标 3D 形状的静止形状是一项涉及逆向设计能力的重要任务。在这项研究中，提出了一个简单而高效的平台来设计从 2D 平面复合膜开始的预编程 3D 形状。通过用一小组有限元模拟训练神经网络，作者能够获得像素化二维弹性膜的最佳设计和变形为目标形状所需的充气压力。所提出的方法具有在多个尺度和不同应用中使用的潜力。举个例子，