Phase Dynamic

Phase dynamics research explores how systems evolve through different stages or phases, focusing on understanding the underlying mechanisms driving these transitions and their implications. Current investigations utilize diverse models, including deep neural networks (DNNs), quantum neural networks, and cellular automata, to analyze phase transitions in various contexts, such as DNN training dynamics, self-attention mechanisms in large language models, and complex fluid flows. These studies aim to improve model efficiency, predictability, and interpretability, with applications ranging from materials science and engineering to neuroscience and early warning systems for critical transitions in complex systems.