Shape Optimization

Shape optimization aims to find the optimal geometry of an object to maximize or minimize a desired performance metric, often involving computationally expensive simulations. Current research heavily utilizes machine learning, employing deep neural networks (including convolutional and physics-informed neural networks), generative adversarial networks, and reinforcement learning algorithms to accelerate the optimization process and handle high-dimensional design spaces. Dimensionality reduction techniques, such as principal component analysis and autoencoders, are also employed to improve efficiency. These advancements are significantly impacting engineering design across various fields, enabling faster and more efficient design cycles for applications ranging from airfoil design to manufacturing processes.