Inferring low-dimensional microstructure representations using convolutional neural networks
| Autor: | Turab Lookman, Kipton Barros, Nicholas Lubbers |
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| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Theoretical computer science Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Materials informatics FOS: Physical sciences 02 engineering and technology 01 natural sciences Convolutional neural network Set (abstract data type) 0103 physical sciences Representation (mathematics) 010302 applied physics Condensed Matter - Materials Science business.industry Deep learning Nonlinear dimensionality reduction Materials Science (cond-mat.mtrl-sci) Pattern recognition Computational Physics (physics.comp-ph) 021001 nanoscience & nanotechnology Embedding Granularity Artificial intelligence 0210 nano-technology business Physics - Computational Physics |
| Zdroj: | Physical review. E. 96(5-1) |
| ISSN: | 2470-0053 |
| Popis: | We apply recent advances in machine learning and computer vision to a central problem in materials informatics: The statistical representation of microstructural images. We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we use manifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone. 14 Pages, 12 Figures |
| Databáze: | OpenAIRE |
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