We present a machine learning framework for predicting the optimized structural topology designs using multiresolution data. Our approach primarily uses optimized designs from inexpensive coarse mesh finite element simulations for model training and generates high resolution images associated with simulation parameters that are not previously used. Our cost-efficient approach enables the designers to effectively search through possible candidate designs in situations where the design requirements rapidly change. The underlying neural network framework is based on a deep disjunctive normal shape model (DDNSM) which learns the mapping between the simulation parameters and segments of multi resolution images. Using this image-based analysis we provide a practical algorithm which enhances the predictability of the learning machine by determining a limited number of important parametric samples (i.e. samples of the simulation parameters) on which the high resolution training data is generated. We demonstrate our approach on benchmark compliance minimization problems including the 3D topology optimization where we show that the high-fidelity designs from the learning machine are close to optimal designs and can be used as effective initial guesses for the large-scale optimization problem.

我们提出了一种机器学习框架，用于使用多分辨率数据预测优化的结构拓扑设计。我们的方法主要使用廉价的粗网格有限元模拟中的优化设计进行模型训练，并生成与以前未使用的模拟参数相关的高分辨率图像。我们的高性价比方法使设计师能够在设计需求快速变化的情况下有效地搜索可能的候选设计。底层的神经网络框架基于深度分离法线形状模型（DDNSM），该模型学习模拟参数与多分辨率图像的各段之间的映射。使用基于图像的分析，我们提供了一种实用的算法，该算法通过确定在其上生成高分辨率训练数据的有限数量的重要参数样本（即模拟参数的样本）来增强学习机的可预测性。我们展示了针对基准合规性最小化问题的方法，包括3D拓扑优化，其中我们证明了学习机的高保真设计接近于最佳设计，可以用作大规模优化问题的有效初始猜测。