About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Hume-Rothery Symposium on First-Principles Materials Design
|
Presentation Title |
Probabilistic Approach to Materials Modeling |
Author(s) |
Fei Zhou |
On-Site Speaker (Planned) |
Fei Zhou |
Abstract Scope |
Material microstructure, which plays a key role in the processing-structure-property relationship of engineering materials, is a challenge for modeling methods due to the high computational expenses associated with the demanding time and length scales. We demonstrate that data-driven scientific machine learning methods provide efficient and accurate surrogate models to accelerate various traditional computational approaches, including phase field, kinetic Monte Carlo, cellular automata and discrete dislocation dynamics. |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Machine Learning, Computational Materials Science & Engineering |