New take on machine learning helps us 'scale up' phase transitions
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 Phys.org - latest science and technology news stories https://ift.tt/34Bb7at
from Phys.org - latest science and technology news stories https://ift.tt/34Bb7at
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