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DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures
Name
43579_2022_171_ReferencePDF.pdf
Size
1.55 MB
Format
Adobe PDF
Checksum (MD5)
1820948c9a6b26d503786a7496daa4b0
Author(s)
Buehler, Markus J.
Date Issued
March 12, 2022
Publisher
Springer International Publishing
Citation
Buehler, Markus J. 2022. "DeepFlames: Neural network-driven self-assembly of flame particles into hierarchical structures."
Version
Author's final manuscript
Abstract
Abstract
The spontaneous assembly of materials from elementary building blocks is one of the most intriguing natural phenomena. Conventional modeling relies physical approaches to examine such processes. In this paper, a framework is proposed to offer an alternative paradigm, via the use of deep learning, and specifically the use of generative adversarial models as well as a combination of natural language processing and transformer neural nets to create hierarchical assemblies of building blocks. We study the assembly of elementary flame particles into hierarchical materials with features across scales, illustrating the Universality–Diversity Principle (UDP), and create novel material using additive manufacturing.
Graphical abstract
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1557/s43579-022-00171-y
