GALCIT Colloquium

Accurate prediction of stress distribution within the microstructure of composites is an

important step in optimizing materials design. Stress distribution drives damage initiation

and evolution, which directly affect the durability and lifespan of composite materials.

Traditional methods, such as finite element analysis (FEA), provide detailed insights into

composite behavior but are often computationally expensive, especially when dealing with

complex geometries and nonlinear material behaviors.

In this talk, I will introduce CompINet, a novel deep learning framework that accelerates

stress analysis in fiber-reinforced composites by serving as a fast surrogate for FEA. Unlike

conventional data-driven approaches that rely on large, computationally intensive datasets,

CompINet requires 20 times less training data. This improvement is achieved through the

incorporation of geometric features of composite microstructures during training. By

combining graph and convolutional neural networks, CompINet effectively captures fiber

interactions and predicts mechanical fields with high accuracy, offering a scalable solution

to complex stress analysis problems.