Predicting stress, strain and deformation fields in materials and structures with graph neural networks.

ABSTRACT: Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The

SUBMITTER: Maurizi M 

PROVIDER: S-EPMC9759553 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature