Machine learning of explicit order parameters: from the Ising model to SU(2) lattice gauge theory
We present a solution to the problem of interpreting neural networks classifying phases of matter. We devise a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently symmetric. In this case...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
8 November 2017
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| In: |
Physical review
Year: 2017, Volume: 96, Issue: 18, Pages: 184410 |
| ISSN: | 2469-9969 |
| DOI: | 10.1103/PhysRevB.96.184410 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1103/PhysRevB.96.184410 Verlag, Volltext: https://link.aps.org/doi/10.1103/PhysRevB.96.184410 
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| Author Notes: | Sebastian J. Wetzel and Manuel Scherzer |
| Summary: | We present a solution to the problem of interpreting neural networks classifying phases of matter. We devise a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently symmetric. In this case one can easily deduce the quantity by which the neural network classifies the input. The method is applied to the Ising model and SU(2) lattice gauge theory. In both systems we deduce the explicit expressions of the order parameters from the decision functions of the neural networks. We assume no prior knowledge about the Hamiltonian or the order parameters except Monte Carlo-sampled configurations. |
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| Item Description: | Gesehen am 23.04.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2469-9969 |
| DOI: | 10.1103/PhysRevB.96.184410 |