Multiscale System

Multiscale systems research focuses on modeling and simulating systems exhibiting dynamics across vastly different spatial or temporal scales, aiming to accurately capture interactions between these scales. Current efforts concentrate on developing data-driven methods, employing machine learning architectures like recurrent neural networks, neural operators, and normalizing flows, often coupled with traditional numerical techniques to improve efficiency and accuracy. These advancements are crucial for addressing computationally expensive problems in diverse fields, including fluid dynamics, materials science, and epidemiology, enabling more accurate predictions and deeper understanding of complex phenomena.