Machine Learning

Machine Learning for Phase Transitions
Data-driven methods based on sample instances of the state of a physical system as a function of the system’s parameters.
FS in Collaboration With Julian Arnold, Flemming Holtorf, Eliska Greplova, Agnes Valenti, Martin Zonda, Axel Lode, Gregor Boschung, Sebastian Huber, And Niels Lörch
Code PRE NJP PRResearch PRX NeurIPS Workshop
Machine Learning for Phase Transitions
F. Schäfer and N. Lörch, Phys. Rev. E 99, 062107 (2019)
We introduce an alternative method to identify phase boundaries in physical systems. It is based on training a predictive model such as …
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