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Register a new userThe typical set and entropy in stochastic systems with arbitrary phase space growth
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The existence of the typical set is key for the consistence of the ensemble formalization of statistical mechanics. We demonstrate here that the typical set can be defined and characterized for a general class of stochastic processes. This includes processes showing arbitrary path dependence, long range correlations or dynamic sampling spaces. We show how the typical set is characterized from general forms of entropy and how one can transform these general entropic forms into extensive functionals and, in some cases, to Shannon path entropy. The definition of the typical set and generalized forms of entropy for systems with arbitrary phase space growth may help to provide an ensemble picture for the thermodynamic paths of many systems away from equilibrium. In particular, we argue that a theory of expanding/shrinking phase spaces in processes displaying an intrinsic degree of stochasticity may lead to new frameworks for exploring the emergence of complexity and robust properties in open ended evolutionary systems and, in particular, of biological systems.
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The stochastic entropy generated during the evolution of a system interacting with an environment may be separated into three components, but only two of these have a non-negative mean. The third component of entropy production is associated with the relaxation of the system probability distribution towards a stationary state and with nonequilibrium constraints within the dynamics that break detailed balance. It exists when at least some of the coordinates of the system phase space change sign under time reversal, and when the stationary state is asymmetric in these coordinates. We illustrate the various components of entropy production, both in detail for particular trajectories and in the mean, using simple systems defined on a discrete phase space of spatial and velocity coordinates. These models capture features of the drift and diffusion of a particle in a physical system, including the processes of injection and removal and the effect of a temperature gradient. The examples demonstrate how entropy production in stochastic thermodynamics depends on the detail that is included in a model of the dynamics of a process. Entropy production from such a perspective is a measure of the failure of such models to meet Loschmidts expectation of dynamic reversibility.
The total entropy production and its three constituent components are described both as fluctuating trajectory-dependent quantities and as averaged contributions in the context of the continuous Markovian dynamics, described by stochastic differential equations with multiplicative noise, of systems with both odd and even coordinates with respect to time reversal, such as dynamics in full phase space. Two of these constituent quantities obey integral fluctuation theorems and are thus rigorously positive in the mean by Jensens inequality. The third, however, is not and furthermore cannot be uniquely associated with irreversibility arising from relaxation, nor with the breakage of detailed balance brought about by non-equilibrium constraints. The properties of the various contributions to total entropy production are explored through the consideration of two examples: steady state heat conduction due to a temperature gradient, and transitions between stationary states of drift-diffusion on a ring, both in the context of the full phase space dynamics of a single Brownian particle.
The quantum entropy-typical subspace theory is specified. It is shown that any mixed state with von Neumann entropy less than h can be preserved approximately by the entropy-typical subspace with entropy= h. This result implies an universal compression scheme for the case that the von Neumann entropy of the source does not exceed h.
Computing the stochastic entropy production associated with the evolution of a stochastic dynamical system is a well-established problem. In a small number of cases such as the Ornstein-Uhlenbeck process, of which we give a complete exposition, the distribution of entropy production can be obtained analytically, but in general it is much harder. A recent development in solving the Fokker-Planck equation, in which the solution is written as a product of positive functions, enables the distribution to be obtained approximately, with the assistance of simple numerical techniques. Using examples in one and higher dimension, we demonstrate how such a framework is very convenient for the computation of stochastic entropy production in diffusion processes.
A change in a stochastic system has three representations: Probabilistic, statistical, and informational: (i) is based on random variable $u(omega)totilde{u}(omega)$; this induces (ii) the probability distributions $F_u(x)to F_{tilde{u}}(x)$, $xinmathbb{R}^n$; and (iii) a change in the probability measure $mathbb{P}totilde{mathbb{P}}$ under the same observable $u(omega)$. In the informational representation a change is quantified by the Radon-Nikodym derivative $lnleft( frac{ d tilde{mathbb{P}}}{ dmathbb{P}}(omega)right)=-lnleft(frac{ d F_u}{ d F_{tilde{u}}}(x)right)$ when $x=u(omega)$. Substituting a random variable into its own density function creates a fluctuating entropy whose expectation has been given by Shannon. Informational representation of a deterministic transformation on $mathbb{R}^n$ reveals entropic and energetic terms, and the notions of configurational entropy of Boltzmann and Gibbs, and potential of mean force of Kirkwood. Mutual information arises for correlated $u(omega)$ and $tilde{u}(omega)$; and a nonequilibrium thermodynamic entropy balance equation is identified.
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