Structural Design

Structural design research is increasingly leveraging machine learning to optimize design processes and generate novel structures, moving beyond traditional iterative methods. Current efforts focus on developing efficient algorithms, such as neural networks and diffusion models, to predict optimal designs for various systems, from continuous beams to complex engineering systems like spacecraft and riboswitches. This work utilizes techniques like Design Structure Matrices (DSMs) to manage complexity and improve efficiency, particularly in large-scale projects. The ultimate goal is to accelerate design cycles, reduce costs, and enable the creation of more innovative and efficient structures across diverse engineering disciplines.