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Utz, Yannick; Wartzack, Sandro J.; Goetz, Stefan

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The applicability and scalability of design adaptations utilizing reinforcement learning can be broadened by using graph-based approaches instead of rigid vector- or grid-based ones. However, graph-based approaches often require a high number of simulations to converge. To reduce the simulation effort in the mechanical optimisation, the reinforcement learning setup is enriched with task-specific causal and physically based information. This work systematically investigates the influence of this additional information on the efficiency of design adaptations using a factorial test design.

Keywords:

design automation, artificial intelligence (AI), reinforcement learning, graph neural networks, information enrichment