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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. ....

Hiroyuki Mori , Yutaka Okabe , Agency For Science , Scientific Research , Tokyo Metropolitan University , Bioinformatics Institute Of Singapore , Research Fellowship For Young Scientists , Research Attachment Programme Of Singapore , Japan Society For The Promotion Of Science , Shibaura Institute Of Technology , Professors Yutaka Okabe , Shibaura Institute , Bioinformatics Institute , Japan Society , Research Fellowship , Young Scientists , Research Attachment Programme , Atmospheric Science , Chemistry Physics Materials Sciences , Earth Science , Temperature Dependent Phenomena , Mathematics Statistics , Systems Chaos Pattern Formation Complexity , Technology Engineering Computer Science , ஹீரோயுக்கி மோரி , யத்க ஒக்கபே ,