About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
|
First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Deep Learning Surrogate Models for Multiscale Simulation of Advanced Materials |
Author(s) |
Cornwell Cornwell, Flavio Souza |
On-Site Speaker (Planned) |
Cornwell Cornwell |
Abstract Scope |
Multiscale modeling has proven to be the most accurate approach for characterizing advanced materials in finite element analysis (FEA). Fully-coupled multiscale analysis utilizes FEA at the material level to represent micromechanical attributes which provides high levels of accuracy compared to analytical constitutive material laws. However, multiscale analysis can be computationally expensive if many microstructural models are run concurrently. Using machine learning, specifically deep-learning, these microstructure models can be replaced with neural networks to predict the constitutive properties of advanced materials. In this paper, many network configurations demonstrate high levels of accuracy and flexibility in predicting multiple microstructural models. Accuracy was evaluated by comparing stress-strain curves of the models compared to results computed through FEA. |
Proceedings Inclusion? |
Undecided |