Structural Optimization

Structural optimization aims to find the best design for a structure, maximizing performance while minimizing material use or other constraints. Current research focuses on applying machine learning techniques, such as reinforcement learning, genetic algorithms, and diffusion models, often coupled with variational autoencoders or graph neural networks, to explore complex design spaces and overcome limitations of traditional methods. These advancements are improving the efficiency and effectiveness of structural design across diverse fields, from robotics and additive manufacturing to drug discovery and signal processing, by enabling the exploration of a wider range of designs and the generation of more robust and optimized solutions.