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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Adversarial Networks for Digital Microstructure Generation
Author(s) Stephen R. Niezgoda, Mengfei Yuan, Dennis Dimiduk, Yunzhi Wang
On-Site Speaker (Planned) Stephen R. Niezgoda
Abstract Scope The microstructures of engineering materials are complex, having detailed crystallography, morphology, and composition. Their responses, especially to extreme-value limited stimuli, depend upon specific aspects of microstructure. Thus, highly-accurate virtual representations are needed as inputs to for modeling and simulation. The challenge to applying data science techniques to microstructure generation is that large amount of training data is typically requires, which is largely intractable due to the large cost of collecting high fidelity microstructural images. In this talk we explore the application of Generative Adversarial Networks (GAN) and related techniques to the generation of digital materials. GANs consist of two independent networks a generator, which creates trial synthetic microstructures, and a discriminator which seeks to identify the synthetic from real images. The two networks learn from each other and by engaging in competition can achieve superior performance, with far less training data, than other deep learning approaches.
Proceedings Inclusion? Definite: At-meeting proceedings


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