Efficient modeling of failure in composite materials with randomly distributed inclusions
Abstract
This poster presents an efficient model for predicting failure in composite materials with randomly distributed inclusions. First, we conduct 2D analyses of stress and displacement fields using the finite element method, with particular emphasis on the material properties of the matrix and inclusions. By leveraging convolutional neural networks, we propose an efficient approach to accelerating these simulations.
Primary Faculty Mentor Name
Ehsan Ghazanfari
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Environmental Engineering
Primary Research Category
Engineering and Math Science
Efficient modeling of failure in composite materials with randomly distributed inclusions
This poster presents an efficient model for predicting failure in composite materials with randomly distributed inclusions. First, we conduct 2D analyses of stress and displacement fields using the finite element method, with particular emphasis on the material properties of the matrix and inclusions. By leveraging convolutional neural networks, we propose an efficient approach to accelerating these simulations.
