Python Data Driven framework for acceleration of Phase-Field simulations[Formula presented]

The passage describes the development of a numerical framework in Python to create and process a large dataset for time-series prediction using Deep Learning algorithms. The dataset is generated by solving the Cahn–Hilliard equation for spinodal decomposition of a binary alloy and is labeled to train the algorithms. Prior to training, dimensionality reduction is performed using Auto-encoders and Principal Component Analysis. The framework identifies three distinct latent dimensions/spaces for the datasets. The primary dataset was generated by running up to 10,000 High-Fidelity Phase-Field simulations in parallel using High-Performance Computing (HPC). The framework is compatible with all major operating systems and has been thoroughly tested on Python 3.7 and later versions.