Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations

TL;DR

This paper presents a neural network-based framework combining auto-encoders, PCA, and LSTM to efficiently reduce dimensionality and predict microstructural images, accelerating phase-field simulations.

Contribution

It introduces a novel data-driven approach that significantly reduces data dimensionality and enables time-series prediction to speed up phase-field simulations.

Findings

Auto-encoders and PCA achieve 1/196 reduction with over 80% accuracy.

LSTM networks can predict subsequent simulation frames.

The framework accelerates simulations without high computational resources.

Abstract

In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduction of simulated microstructural images and to provide time-series analysis. The dataset was indeed constructed from high-fidelity Phase-Field simulations. Analyses demonstrated that the association of auto-encoder neural networks and principal component analyses leads to ensure efficient and significant dimensionality reduction: 1/196 of reduction ratio with more than 80% of accuracy. These findings give insight to apply analyses on data from the latent dimension. Application of Long Short Term Memory (LSTM) neural networks showed the possibility of making next frame predictions; that makes possible the acceleration of Phase-Field simulation without the need of high computing resources. We discussed the application of such a framework on various areas of research. Different methods are proposed from the conducted analyses, in order to ensure dimensionality reduction, including auto-encoders, principal component analysis and Artificial Neural Networks, and time-series analysis, including LSTM and Gated Recurrent Unit (GRU).