|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Accelerating Phase-field Based Predictions via Surrogate Models Trained by Machine Learning Methods
||Remi Dingreville, David Montes de Oca Zapiain, James A Stewart, Chongze Hu, Shawn Martin
|On-Site Speaker (Planned)
The phase-field method is a powerful and versatile computational approach for modeling the evolution of the microstructure and properties of a wide variety of physical, chemical and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve useful degree of accuracy. In this talk, I will discuss advanced in developing computationally inexpensive and accurate, data-driven surrogate models that directly learn the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. I will discuss the advantages/disadvantages of combining various techniques that integrate low-dimensional description of the microstructure, obtained directly from phase-field simulations, with history-dependent deep neural network. Lasty, I will give examples on the performance and accuracy of the established machine-learning, accelerated framework to predict the non-linear microstructure evolution as compared to high-fidelity phase-field. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.
||Computational Materials Science & Engineering, Machine Learning, Phase Transformations