Multiscale Modeling

Multiscale modeling aims to simulate complex systems by integrating information across different spatial and temporal scales, bridging the gap between microscopic details and macroscopic behavior. Current research emphasizes the development and application of hybrid models, combining established methods like finite element analysis with machine learning techniques such as graph neural networks, deep operator networks, and transformers, to improve accuracy and efficiency. This approach is proving valuable in diverse fields, from materials science and fluid dynamics to biological systems and climate modeling, enabling more accurate predictions and a deeper understanding of complex phenomena.