Polymers, Год журнала: 2025, Номер 17(8), С. 1108 - 1108

Опубликована: Апрель 19, 2025

The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity multi-scale behavior. Traditional computational methods, while effective, often struggle balance accuracy with efficiency, especially when bridging the atomistic macroscopic scales. Recently, physics-informed neural networks (PINNs) have emerged as a promising tool that integrates data-driven learning governing physical laws system. This review discusses development application PINNs in context science. It summarizes recent advances, outlines key methodologies, analyzes benefits limitations using for property prediction, structural design, process optimization. Finally, it identifies current future research directions further leverage advanced modeling.

Язык: Английский