  In this paper, a mechanistic data-driven approach is proposed to accelerate
structural topology optimization, employing an in-house developed finite
element convolutional neural network (FE-CNN). Our approach can be divided into
two stages: offline training, and online optimization. During offline training,
a mapping function is built between high and low resolution representations of
a given design domain. The mapping is expressed by a FE-CNN, which targets a
common objective function value (e.g., structural compliance) across design
domains of differing resolutions. During online optimization, an arbitrary
design domain of high resolution is reduced to low resolution through the
trained mapping function. The original high-resolution domain is thus designed
by computations performed on only the low-resolution version, followed by an
inverse mapping back to the high-resolution domain. Numerical examples
demonstrate that this approach can accelerate optimization by up to an order of
magnitude in computational time. Our proposed approach therefore shows great
potential to overcome the curse-of-dimensionality incurred by density-based
structural topology optimization. The limitation of our present approach is
also discussed.
