An adaptive and scalable artificial neural network-based model-order-reduction method for large-scale topology optimization designs

Topology optimization (TO) provides a systematic approach for obtaining structure design with optimum performance of interest. However, the process requires the numerical evaluation of the objective function and constraints at each iteration, which is computationally expensive, especially for large-scale designs. Deep learning-based models have been developed to accelerate the process either by acting as surrogate models replacing the simulation process, or completely replacing the optimization process. However, most of them require a large set of labelled training data, which is generated mostly through simulations. The data generation time scales rapidly with the design size, decreasing the efficiency of the method itself. Another major issue is the weak generalizability of deep learning models. Most models are trained to work with the design problem similar to that used for data generation and require retraining if the design problem changes. In this work an adaptive, scalable deep learning-based model-order-reduction method is proposed to accelerate large-scale TO process, by utilizing MapNet, a neural network which maps the field of interest from coarse-scale to fine-scale. The proposed method allows for each simulation of the TO process to be performed at a coarser mesh, thereby greatly reducing the total computational time. More importantly, a crucial element, domain fragmentation, is introduced and integrated into the method, which greatly improves the transferability and scalability of the method. It has been demonstrated that the MapNet trained using data from one cantilever beam design with a specific loading condition can be directly applied to other structure design problems with different domain shapes, sizes, boundary and loading conditions.