Macroscopic Change

Macroscopic change research focuses on understanding and predicting large-scale behaviors arising from the interactions of numerous microscopic components, a challenge addressed through computational modeling and machine learning. Current efforts utilize deep learning, particularly transformer-based architectures and techniques like the analysis of macroscopic limits in neural network training, to improve the efficiency and accuracy of simulations across diverse systems, from neural networks to physical phenomena like swarming behavior. These advancements offer improved detection of subtle macroscopic changes in complex systems, with applications ranging from materials science to human-machine interfaces, enhancing our ability to analyze and control intricate processes.