| About this Abstract |
| Meeting |
MS&T21: Materials Science & Technology
|
| Symposium
|
Materials Informatics for Images and Multi-dimensional Datasets
|
| Presentation Title |
Quantitative Comparisons of 2D Microstructures with the Wasserstein Metric |
| Author(s) |
Ethan Suwandi, Jeremy Mason |
| On-Site Speaker (Planned) |
Ethan Suwandi |
| Abstract Scope |
Comparison of material microstructures is traditionally done using a selection of incomplete and ad-hoc statistics. With the advent of computational materials design, this has encouraged the development of microstructure generation techniques which produce synthetic microstructures that do not replicate a physical process. A more robust method of comparison would be useful both to compare experimental microstructures with emerging standards and to validate synthetic microstructure generation techniques. We propose using a balanced Wasserstein distance to quantify the difference between two micrographs with respect to both geometry and size simultaneously, where the measured is given by an inverted unsigned distance function to the grain boundary network. By finding the best pairwise matching of sets of micrographs taken from two microstructures, an overall similarity metric is established. This method is employed to compare a number of synthetic microstructures generated with similar starting statistics and varying features. |