# Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models

arXiv: https://arxiv.org/abs/2511.11630 · HTML: https://mlsanity.com/paper/2511.11630/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 82 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://mlsanity.com/paper/2001.08317/) (2020)
- [Time Series Forecasting Using LSTM Networks: A Symbolic Approach](https://mlsanity.com/paper/2003.05672/) (2020)
- [Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs](https://mlsanity.com/paper/1603.00423/) (2016)
- [Time Series Forecasting (TSF) Using Various Deep Learning Models](https://mlsanity.com/paper/2204.11115/) (2022)
- [High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations](https://mlsanity.com/paper/2505.05354/) (2025)
- [Kinetic equations and level-set approach for simulating solid-state microstructure evolutions at the mesoscopic scale: state of the art, limitations, and prospects](https://mlsanity.com/paper/2310.04421/) (2023)
- [A novel highly efficient Lagrangian model for massively multidomain simulations: parallel context](https://mlsanity.com/paper/2009.04424/) (2020)
- [Statistical behaviour of interfaces subjected to curvature flow and torque effects applied to microstructural evolutions](https://mlsanity.com/paper/2106.05605/) (2021)
- [Efficient and accurate simulation of the Smith-Zener pinning mechanism during grain growth using a front-tracking numerical framework](https://mlsanity.com/paper/2501.11130/) (2025)
- [Vertex and front-tracking methods for the modeling of microstructure evolution at the solid state: a brief review](https://mlsanity.com/paper/2510.21818/) (2025)

## Abstract (arXiv)

Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning approaches were evaluated, including recurrent neural networks (RNN), long short-term memory (LSTM), temporal convolutional networks (TCN), and transformers, to forecast grain size distributions during grain growth. Unlike full-field simulations, which are computationally demanding, the present work relies on mean-field statistical descriptors extracted from high-fidelity simulations. A dataset of 120 grain growth sequences was processed into normalized grain size distributions as a function of time. The models were trained to predict future distributions from a short temporal history using a recursive forecasting strategy. Among the tested models, the LSTM network achieved the highest accuracy (above 90%) and the most stable performance, maintaining physically consistent predictions over extended horizons while reducing computation time from about 20 minutes per sequence to only a few seconds, whereas the other architectures tended to diverge when forecasting further in time. These results highlight the potential of low-dimensional descriptors and LSTM-based forecasting for efficient and accurate microstructure prediction, with direct implications for digital twin development and process optimization.