Coarsegrained

Coarse-grained describes models, descriptions, or measurements that deliberately remove fine-grained details to emphasize larger-scale behavior. The approach is used across physics, chemistry, biology, and data science to manage complexity and reveal emergent properties that are not apparent at the microscopic level.

In statistical mechanics and field theory, coarse-graining is the process of mapping a detailed microscopic description to a simpler effective theory by averaging over short-range fluctuations. Techniques include block averaging and renormalization group transformations, which aim to preserve long-wavelength observables such as conserved quantities and symmetries while reducing the number of degrees of freedom.

In molecular modeling, coarse-grained (CG) models replace groups of atoms with larger interaction sites or beads. This reduces computational cost and allows simulation of larger systems or longer timescales. Popular examples include the MARTINI force field and various polymer or protein models. CG models require parameterization to reproduce target properties from higher-resolution models or experiments, and they trade atomistic detail for speed and transferability.

In data analysis and image processing, coarse-graining refers to aggregating data over small regions to produce multi-resolution representations, as in pooling operations or hierarchical models. This enables scalable analysis and can reveal trends when fine detail is noisy or unnecessary.

See also: coarse-graining, multi-scale modeling, renormalization, upscaling.