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Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system

Corneel Casert (UGent) , Tom Vieijra (UGent) , Jannes Nys (UGent) and Jan Ryckebusch (UGent)
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Abstract
Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and interpretability properties of learning algorithms. To this end, we use supervised and unsupervised learning to infer the phase boundaries of the active Ising model, starting from an ensemble of configurations of the system. We illustrate that unsupervised learning techniques are powerful at identifying the phase boundaries in the control parameter space, even in situations of phase coexistence. It is demonstrated that supervised learning with neural networks is capable of learning the characteristics of the phase diagram, such that the knowledge obtained at a limited set of control variables can be used to determine the phase boundaries across the phase diagram. In this way, we show that properly designed supervised learning provides predictive power to regions in the phase diagram that are not included in the training phase of the algorithm. We stress the importance of introducing interpretability methods in order to perform a physically relevant classification of the phases with deep learning.

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Please use this url to cite or link to this publication:

Chicago
Casert, Corneel, Tom Vieijra, Jannes Nys, and Jan Ryckebusch. 2019. “Interpretable Machine Learning for Inferring the Phase Boundaries in a Nonequilibrium System.” Physical Review E 99 (2).
APA
Casert, C., Vieijra, T., Nys, J., & Ryckebusch, J. (2019). Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system. PHYSICAL REVIEW E, 99(2).
Vancouver
1.
Casert C, Vieijra T, Nys J, Ryckebusch J. Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system. PHYSICAL REVIEW E. 2019;99(2).
MLA
Casert, Corneel et al. “Interpretable Machine Learning for Inferring the Phase Boundaries in a Nonequilibrium System.” PHYSICAL REVIEW E 99.2 (2019): n. pag. Print.
@article{8603794,
  abstract     = {Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and interpretability properties of learning algorithms. To this end, we use supervised and unsupervised learning to infer the phase boundaries of the active Ising model, starting from an ensemble of configurations of the system. We illustrate that unsupervised learning techniques are powerful at identifying the phase boundaries in the control parameter space, even in situations of phase coexistence. It is demonstrated that supervised learning with neural networks is capable of learning the characteristics of the phase diagram, such that the knowledge obtained at a limited set of control variables can be used to determine the phase boundaries across the phase diagram. In this way, we show that properly designed supervised learning provides predictive power to regions in the phase diagram that are not included in the training phase of the algorithm. We stress the importance of introducing interpretability methods in order to perform a physically relevant classification of the phases with deep learning.},
  articleno    = {023304},
  author       = {Casert, Corneel and Vieijra, Tom and Nys, Jannes and Ryckebusch, Jan},
  issn         = {2470-0045},
  journal      = {PHYSICAL REVIEW E},
  language     = {eng},
  number       = {2},
  pages        = {7},
  title        = {Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system},
  url          = {http://dx.doi.org/10.1103/PhysRevE.99.023304},
  volume       = {99},
  year         = {2019},
}

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