23 citations · 72 across the 10 of their papers we have counts for
4 papers · 1 filter
Machine learning the Ising transition: A comparison between discriminative and generative approaches
Difei Zhang, Frank Schäfer, Julian Arnold
The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems ca…
Machine learning phase transitions: Connections to the Fisher information
Julian Arnold, Niels Lörch, Flemming Holtorf +1
Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here…
Replacing neural networks by optimal analytical predictors for the detection of phase transitions
Julian Arnold, Frank Schäfer
Identifying phase transitions and classifying phases of matter is central to understanding the properties and behavior of a broad range of material systems. In recent years, machin…
Interpretable and unsupervised phase classification
Julian Arnold, Frank Schäfer, Martin Žonda +1
Fully automated classification methods that yield direct physical insights into phase diagrams are of current interest. Here, we demonstrate an unsupervised machine learning method…