Accelerating Phase-Field Based Predictions via Surrogate Models Trained by Machine Learning Methods

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 model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine learning techniques. I discuss the advantages/disadvantages of combining various techniques to 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.<br/>Computational capabilities were supported by the Center for Integrated Nanotechnologies, an Office of Science user facility operated for the U.S. Department of Energy. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy National Nuclear Security Administration under contract DE-NA0003525. The views expressed in this article do not necessarily represent the views of the US DOE or the US Government.