The paper introduces SOgym, an open-source reinforcement learning environment for topology optimization.

This paper presents YOLOv8-TO, a novel method using a custom YOLOv8 model to automate the reverse engineering of topology-optimized structures into parametric forms, significantly outperforming traditional skeletonization methods in accuracy and efficiency.

The study presents complexity-driven layout exploration for aircraft structures (CD-LEAS), a novel process for efficient topology optimization. Case studies confirm CD-LEAS’s ability to produce simple, light, stiff, and buckling-resistant layouts.

This research leverages a deep learning model to improve scalability in topology optimization, with a new approach that reduces computation time by 36.84% and maintains mechanical performance.

Comparison of first-order optimizers for the Moving Morphable Components topology optimization framework. Hybrid MMA-GCMMA optimizer is shown to have a better convergence behaviour for the MMC framework.