  Topology Optimization (TO) provides a systematic approach for obtaining
structure design with optimum performance of interest. However, the process
requires numerical evaluation of objective function and constraints at each
iteration, which is computational expensive especially for large-scale design.
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 are generated mostly through simulations. The
data generation time scales rapidly with the design domain size, decreasing the
efficiency of the method itself. Another major issue is the weak
generalizability of most 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 a 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. Moreover, by using
domain fragmentation, the transferability of the MapNet is largely improved.
Specifically, 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.
