Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

Simple item page
dc.citation.articleNumber025027en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleMachine Learning: Science and Technologyen_US
dc.citation.volumeNumber5en_US
dc.contributor.authorFan, Shaoxunen_US
dc.contributor.authorHitt, Andrew L.en_US
dc.contributor.authorTang, Mingen_US
dc.contributor.authorSadigh, Babaken_US
dc.contributor.authorZhou, Feien_US
dc.date.accessioned2024-08-02T13:32:08Zen_US
dc.date.available2024-08-02T13:32:08Zen_US
dc.date.issued2024en_US
dc.description.abstractSurrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.en_US
dc.identifier.citationFan, S., Hitt, A. L., Tang, M., Sadigh, B., & Zhou, F. (2024). Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution. Machine Learning: Science and Technology, 5(2), 025027. https://doi.org/10.1088/2632-2153/ad3e4ben_US
dc.identifier.digitalFan_2024_Mach_Learn__Sci_Technol_5_025027en_US
dc.identifier.doihttps://doi.org/10.1088/2632-2153/ad3e4ben_US
dc.identifier.urihttps://hdl.handle.net/1911/117557en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleAccelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolutionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Fan_2024_Mach_Learn__Sci_Technol_5_025027.pdf
Size:
3.81 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
Materials Science and NanoEngineering Publications