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Found 6 papers, 5 papers with code
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena.
Unsupervised machine learning of topological phase transitions from experimental data
Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and without the knowledge of the order parameter.
Anomaly Detection
Quantum Gases
Disordered Systems and Neural Networks
Mesoscale and Nanoscale Physics
Quantum Physics
Phase Detection with Neural Networks: Interpreting the Black Box
Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions.
Quantum Physics Disordered Systems and Neural Networks
Circular dichroism in high-order harmonic generation: Heralding topological phases and transitions in Chern insulators
Topological materials are of interest to both fundamental science and advanced technologies, because topological states are robust with respect to perturbations and dissipation.
Mesoscale and Nanoscale Physics Quantum Physics
Automated discovery of characteristic features of phase transitions in many-body localization
We identify a new "order parameter" for the disorder driven many-body localization (MBL) transition by leveraging artificial intelligence.
Quantum Physics Disordered Systems and Neural Networks
Identifying Quantum Phase Transitions with Adversarial Neural Networks
Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture.
