About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Physics-informed Data-driven Machine Learning Approach for Mesoscale Materials Science |
Author(s) |
Reeju Pokharel, Anup Pandey, Alexander Scheinker |
On-Site Speaker (Planned) |
Reeju Pokharel |
Abstract Scope |
Macroscale properties and performance of structural materials are influenced by mesoscale interactions between crystallographic grain boundaries, defects, and dislocations. Advanced experimental techniques developed in the last several decades have provided destructive or non-destructive characterization of mesoscale microstructures, micro-mechanical fields and their evolution under various thermo-mechanical conditions. One of the major challenges faced by the advanced imaging and diffraction techniques is the slow analysis and interpretation of experimental data. This has limited our ability to use experimental observations for informing and validating mesoscale deformation and damage models. To address this, we are working on developing a hybrid data-driven physics-informed machine learning approach to speed up data collection and reconstruction beyond the current-state-of-the-art, which will enable accelerated design and deployment of new materials. |
Proceedings Inclusion? |
Planned: |
Keywords |
Characterization, Machine Learning, Modeling and Simulation |