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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]