The maximum entropy principle for non-equilibrium phase transitions: Determination of order parameters, slaved modes, and emerging patterns
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The maximum entropy principle allows one to make guesses on the distribution function of systems by maximizing the information entropy under given constraints. In a previous paper we succeeded to formulate appropriate constraints for systems undergoing nonequilibrium phase transitions, but we had to confine our treatment to the order parameters. In this paper we describe a formalism which does not require any a priori knowledge on the order parameters but rather allows us to determine these as well as the slaved modes and the emerging patterns. The method is applicable also to non-physical systems such as neural nets. Our approach allows us to reconsider the Landau theory of phase transitions from a new point of view. A guess is made on the Fokker-Planck equation underlying the processes which give rise to stationary distribution functions of a single order parameter.
KeywordsSpectroscopy Entropy Neural Network Phase Transition Distribution Function
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