  The question of how methods from the field of artificial intelligence can
help improve the conventional frameworks for topology optimisation has received
increasing attention over the last few years. Motivated by the capabilities of
neural networks in image analysis, different model-variations aimed at
obtaining iteration-free topology optimisation have been proposed with varying
success. Other works focused on speed-up through replacing expensive optimisers
and state solvers, or reducing the design-space have been attempted, but have
not yet received the same attention. The portfolio of articles presenting
different applications has as such become extensive, but few real breakthroughs
have yet been celebrated. An overall trend in the literature is the strong
faith in the "magic" of artificial intelligence and thus misunderstandings
about the capabilities of such methods. The aim of this article is therefore to
present a critical review of the current state of research in this field. To
this end, an overview of the different model-applications is presented, and
efforts are made to identify reasons for the overall lack of convincing
success. A thorough analysis identifies and differentiates between problematic
and promising aspects of existing models. The resulting findings are used to
detail recommendations believed to encourage avenues of potential scientific
progress for further research within the field.
