=============================================================================================
A new efficient grain growth model using a random Gaussian-sampled mode filter

IF THIS CODE IS USED FOR A RESEARCH PUBLICATION, please cite:
    Melville, J., Yadav, V., Yang, L., Krause, A. R., Tonks, M. R., & Harley, J. B. (2024). A new efficient grain growth model using a random Gaussian-sampled mode filter. Materials & Design, 237, 112604.
=============================================================================================

DESCRIPTION:
	This paper presents the use of a Gaussian neighborhood mode filter for predicting grain growth in a manner similar to the solutions obtained by a Monte Carlo Potts model. This flexible grain growth model can quickly utilize modern, computationally optimized data science strategies on graphics processing units to simulate grain growth up to 100 times faster than the state-of-the-art, publicly available Monte Carlo Potts model. We show that, given the correct neighborhood, the mode filter can replicate normal grain growth in two or three dimensions. In addition, the paper briefly demonstrates the ability to model limited anisotropic in grain boundary energy and mobility. Anisotropic grain boundary energy is modeled by defining a weighted mode filter operation. Anisotropic grain boundary mobility is modeled by scaling and orienting the Gaussian neighborhood in a particular direction.
	
CONTRIBUTORS: 
	Joseph Melville [1], Vishal Yadav [2], Lin Yang [2], Amanda R Krause [3], Michael R Tonks [2], Joel B Harley [1]
	
AFFILIATIONS:
	1. University of Florida, SmartDATA Lab, Department of Electrical and Computer Engineering
	2. University of Florida, Tonks Research Group, Department of Material Science and Engineering
	3. Carnegie Mellon University, Department of Materials Science and Engineering

FUNDING SPONSORS:
	U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award \#DE-SC0020384
	U.S. Department of Defence through a Science, Mathematics, and Research for Transformation (SMART) scholarship