  In this publication, an AI-assisted design method based on topology
optimization is presented, which is able to obtain optimized designs in a
direct way, without iterative optimum search. The optimized designs are
provided by an artificial neural network, the predictor, on the basis of
boundary conditions and degree of filling (the volume percentage filled by
material) as input data. In the training phase, geometries generated on the
basis of random input data are evaluated with respect to given criteria and the
results of those evaluations flow into an objective function which is minimized
by adapting the predictor's parameters. Other than in state-of-the-art
procedures, no pre-optimized geometries are used during training.
  After the training is completed, the presented AI-assisted design procedure
supplies geometries which are similar to the ones generated by conventional
topology optimizers, but requires a small fraction of the computational effort
required by those algorithms.
