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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Machine learning phase transition: An iterative proposal
ترجمه فارسی عنوان مقاله:
فاز انتقال یادگیری ماشین: یک پیشنهاد تکرار شونده
منبع:
Sciencedirect - Elsevier - Annals of Physics, Accepted manuscript, 167938: doi:10:1016/j:aop:2019:167938
نویسنده:
X.L. Zhao, L.B. Fu
چکیده انگلیسی:
We propose an iterative proposal to estimate critical points for statistical models based on configurations
by combing machine-learning tools. Firstly, phase scenarios and preliminary boundaries
of phases are obtained by dimensionality-reduction techniques. Besides, this step not only provides
labelled samples for the subsequent step but also is necessary for its application to novel statistical
models. Secondly, making use of these samples as training set, neural networks are employed to
assign labels to those samples between the phase boundaries in an iterative manner. Newly labelled
samples would be put in the training set used in subsequent training and the phase boundaries
would be updated as well. The average of the phase boundaries is expected to converge to the
critical temperature in this proposal. In concrete examples, we implement this proposal to estimate
the critical temperatures for two q-state Potts models with continuous and first order phase transitions.
Linear and manifold dimensionality-reduction techniques are employed in the first step. Both
a convolutional neural network and a bidirectional recurrent neural network with long short-term
memory units perform well for two Potts models in the second step. The convergent behaviors of
the estimations reflect the types of phase transitions. And the results indicate that our proposal
may be used to explore phase transitions for new general statistical models.
قیمت: رایگان
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