| Abstract Scope |
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 a useful degree of accuracy. In this presentation, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine learning techniques. We integrate a statistically-representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a Time-Series Multivariate Adaptive Regression Splines (TSMARS) autoregressive algorithm or a Long Short-Term Memory (LSTM) neural network. Our machine-learning-trained surrogate model shows the best performance and accurately predicts the non-linear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for "on-the-fly" solutions of the phase-field equations-of-motion. |