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FM: Fall Meeting

FM 55: Quantum & Information Science: Neural Networks, Machine Learning, and Artificial Intelligence II

FM 55.4: Talk

Mittwoch, 25. September 2019, 15:00–15:15, 1098

Vector field divergence of predictive model output as indication of phase transitions — •Frank Schäfer and Niels Lörch — Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland

We introduce an alternative method to identify phase boundaries in physical systems. It is based on training a predictive model such as a neural network to infer a physical system's parameters from its state. The deviation of the inferred parameters from the underlying correct parameters will be most susceptible and diverge maximally in the vicinity of phase boundaries. Therefore, peaks in the vector field divergence of the model's predictions are used as indication of phase transitions. Our method is applicable for phase diagrams of arbitrary parameter dimension and without prior information about the phases. Application to both the two-dimensional Ising model and the dissipative Kuramoto-Hopf model show promising results.