This paper introduces an adaptive convolutional neural network (CNN) architecture capable of automating various topology optimization (TO) problems with diverse underlying physics. The proposed architecture has an encoder-decoder-type structure with dense layers added at the bottleneck region to capture complex geometrical features. The network is trained using datasets obtained by the problem-specific open-source TO codes. Tensorflow and Keras are the main libraries employed to develop and to train the model. Effectiveness and robustness of the proposed adaptive CNN model are demonstrated through its performance in compliance minimization problems involving constant and design-dependent loads and in addressing bulk modulus optimization. Once trained, the model takes user's input of the volume fraction as an image and instantly generates an output image of optimized design. The proposed CNN produces high-quality results resembling those obtained via open-source TO codes with negligible performance and volume fraction errors. The paper includes complete associated Python code (Appendix A) for the proposed CNN architecture and explains each part of the code to facilitate reproducibility and ease of learning.

翻译：摘要：本文介绍了一种自适应卷积神经网络架构，能够自动化处理具有不同底层物理机制的各种拓扑优化问题。该架构采用编码器-解码器结构，并在瓶颈区域加入密集层以捕捉复杂几何特征。网络使用基于特定问题的开源拓扑优化代码生成的数据集进行训练，主要利用Tensorflow和Keras库进行模型开发与训练。通过涉及恒定载荷与设计相关载荷的柔度最小化问题以及体模量优化任务，验证了所提出自适应CNN模型的有效性与鲁棒性。训练完成后，该模型可将用户输入的体积分数图像即时转化为优化设计输出图像。所提出的CNN生成的高质量结果与开源拓扑优化代码所得结果高度一致，性能误差和体积分数误差均可忽略。本文提供了所提CNN架构的完整Python代码（附录A），并对代码各部分进行详细解释，以促进可重复性与学习便利性。