Researchers from Tokyo Metropolitan University have enhanced "super-resolution" machine learning techniques to study phase transitions. They identified key features of how large arrays of interacting particles behave at different temperatures by simulating tiny arrays before using a convolutional neural network to generate a good estimate of what a larger array would look like using correlation configurations. The massive saving in computational cost may realize unique ways of understanding how materials behave.
from General Physics News - Science News, Physics News, Physics, Material Sciences, Science https://ift.tt/34Bb7at
No comments:
Post a Comment