Corpus ID: 236134041

Establishing process-structure linkages using Generative Adversarial Networks

@article{Safiuddin2021EstablishingPL,
  title={Establishing process-structure linkages using Generative Adversarial Networks},
  author={Mohammad Safiuddin and CH Likith Reddy and Ganesh Vasantada and Chjns Harsha and Srinu Gangolu},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.09402}
}
The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a crucial task in material design and is of interest in many engineering applications. We develop a GAN (Generative Adversarial Network) to synthesize microstructures based on given processing conditions. This approach is devoid of feature engineering, needs little domain awareness… Expand

References

SHOWING 1-10 OF 15 REFERENCES
Microstructural Materials Design Via Deep Adversarial Learning Methodology
TLDR
The proposed deep adversarial learning methodology is scalable to generate arbitrary sized microstructures, and it can serve as a pre-trained model to improve the performance of a structure-property predictive model via transfer learning. Expand
Material structure-property linkages using three-dimensional convolutional neural networks
Abstract The core materials knowledge needed in the accelerated design, development, and deployment of new and improved materials is most accessible when cast in the form of computationally low costExpand
Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model
TLDR
This work proposes a generative machine learning model that creates an arbitrary amount of artificial material samples with negligible computation cost, when trained on only a limited amount of authentic samples, and introduces a morphology constraint to the training of the generative model, that enforces the resultant artificialmaterial samples to have the same morphology distribution as the authentic ones. Expand
Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures
Abstract We introduce a microstructure dataset focusing on complex, hierarchical structures found in a single Ultrahigh carbon steel under a range of heat treatments. Applying image representationsExpand
Microstructure Sensitive Design for Performance Optimization
TLDR
This review presents the MSD framework in the context of both the engineering advances that have led to its creation, and those that complement or provide alternative methods for design of materials (meaning ‘optimization of material structure’ in this context). Expand
Large Scale GAN Training for High Fidelity Natural Image Synthesis
TLDR
It is found that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Expand
Improved Techniques for Training GANs
TLDR
This work focuses on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic, and presents ImageNet samples with unprecedented resolution and shows that the methods enable the model to learn recognizable features of ImageNet classes. Expand
Computational Design of Hierarchically Structured Materials
A systems approach that integrates processing, structure, property, and performance relations has been used in the conceptual design of multilevel-structured materials. For high-performance alloyExpand
Conditional Generative Adversarial Nets
TLDR
The conditional version of generative adversarial nets is introduced, which can be constructed by simply feeding the data, y, to the generator and discriminator, and it is shown that this model can generate MNIST digits conditioned on class labels. Expand
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Expand
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