Scale Dynamic

Scale dynamics research focuses on efficiently modeling and simulating systems exhibiting behavior across vastly different spatial and temporal scales, a challenge pervasive in fields from neuroscience to fluid dynamics. Current efforts leverage machine learning, particularly deep learning architectures like convolutional neural networks and transformers, often coupled with physics-informed neural networks or large eddy simulation techniques, to create computationally tractable models that capture both coarse-grained and fine-grained dynamics. These advancements enable more accurate and efficient simulations of complex systems, impacting diverse applications including brain-computer interfaces, robotics, and the study of turbulence and molecular dynamics.