Physics-informed neural networks and autoencoders trained on synthetic data locate phase boundaries in the diluted Ising model by exploiting symmetry-breaking biases.
Kernel methods for interpretable machine learning of order parameters.Physical Review B, 96(20):205146
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.str-el 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Uncovering Magnetic Phases with Synthetic Data and Physics-Informed Training
Physics-informed neural networks and autoencoders trained on synthetic data locate phase boundaries in the diluted Ising model by exploiting symmetry-breaking biases.