State Transformation

State transformation research focuses on understanding and modeling how systems change their state over time, aiming to create robust and efficient representations of these changes. Current research emphasizes learning state-invariant representations using techniques like graph neural networks and diffusion models, and explores the role of symmetries and expressive power in learning general policies from data. This work has significant implications for various fields, including object recognition, fine-grained classification, and the development of more efficient and reliable digital twins for complex systems.