In this paper, we propose the use of deep learning-based image super-resolution to accelerate structural topology optimization. Topology optimization suffers from iterative computation and a time cost that increases with the number of elements. Recently, there have been attempts to accelerate topology optimization using deep learning-based models, but they often do not address a wide range of physical conditions or require extensive training data. This highlights the need for an approach that can effectively solve different problem conditions with less training data. Our approach first starts topology optimization at a low resolution to quickly obtain an optimized structure. It is converted to the structure at a higher resolution by using an image super-resolution model. Then, the final structure is obtained by performing topology optimization at this high resolution by using the converted structure as the starting configuration. The super-resolution model learns how to transform low-resolution structural features into high-resolution ones regardless of optimization conditions. As a result, it can be applied to any problems different from the problem used to generate the data for training the model. The proposed approach's excellent acceleration and generalization performance is demonstrated for three representative problems in structural topology optimization with a small number of training datasets. Furthermore, it has been shown to be effective in the deck arch bridge problem and the natural frequency maximization problem. Finally, the proposed approach is also found to be effective on the three-dimensional cantilever beam problem.

中文翻译：

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使用基于深度学习的图像超分辨率加速拓扑优化

在本文中，我们提出使用基于深度学习的图像超分辨率来加速结构拓扑优化。拓扑优化会受到迭代计算的影响，并且时间成本会随着元素数量的增加而增加。最近，有人尝试使用基于深度学习的模型来加速拓扑优化，但它们通常不能解决广泛的物理条件或需要大量的训练数据。这凸显了需要一种能够用更少的训练数据有效解决不同问题条件的方法。我们的方法首先以低分辨率开始拓扑优化，以快速获得优化的结构。通过使用图像超分辨率模型将其转换为更高分辨率的结构。然后，使用转换后的结构作为起始配置，通过在该高分辨率下进行拓扑优化来获得最终结构。超分辨率模型学习如何将低分辨率结构特征转换为高分辨率结构特征，而不管优化条件如何。因此，它可以应用于与生成用于训练模型的数据的问题不同的任何问题。该方法通过少量训练数据集针对结构拓扑优化中的三个代表性问题展示了出色的加速和泛化性能。此外，它已被证明在桥面拱桥问题和固有频率最大化问题上是有效的。最后，所提出的方法也被发现对三维悬臂梁问题有效。