About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithms Development in Materials Science and Engineering
|
| Presentation Title |
A Coupled Thermal-Mechanical Deep Material Network |
| Author(s) |
Ashley Lenau, Andreas Robertson, Dongil Shin, Ricardo Lebensohn, Remi Dingreville |
| On-Site Speaker (Planned) |
Ashley Lenau |
| Abstract Scope |
Deep material networks (DMN) are tree-like machine learning networks that train on linear homogenization relationships for a given microstructure, and then act as a representative volume element to predict non-linear material responses. Most DMNs are utilized for “single physics” problems, such as a DMN predicting the thermal or mechanical response using homogenized thermal conductivity or stiffness values, respectively. However, a DMN architecture or training strategy involving multi-physics homogenization tasks is still not yet well established. Combining thermal and mechanical tasks will ultimately result in a better description of the deformation behavior of the composite for a wider variety of boundary conditions. In this study, a DMN is simultaneously trained to homogenize the stiffness and thermal conductivity of a composite. After training, the network extrapolates stress, strain, heat flux, and temperature gradient for non-linear thermal-mechanical relationships. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Composites |