Coarse Graining

Coarse graining is a computational technique that simplifies complex systems, such as molecules or urban areas, by reducing the number of variables while preserving essential features. Current research focuses on improving the accuracy and transferability of coarse-grained models, employing machine learning approaches like graph neural networks and normalizing flows to learn effective interactions and generate realistic representations. These advancements are significantly impacting fields like molecular dynamics simulations, drug discovery, and urban planning by enabling faster and more efficient analyses of large-scale systems.