3,692 research outputs found
Fluctuation Theorems for Entropy Production and Heat Dissipation in Periodically Driven Markov Chains
Asymptotic fluctuation theorems are statements of a Gallavotti-Cohen symmetry
in the rate function of either the time-averaged entropy production or heat
dissipation of a process. Such theorems have been proved for various general
classes of continuous-time deterministic and stochastic processes, but always
under the assumption that the forces driving the system are time independent,
and often relying on the existence of a limiting ergodic distribution. In this
paper we extend the asymptotic fluctuation theorem for the first time to
inhomogeneous continuous-time processes without a stationary distribution,
considering specifically a finite state Markov chain driven by periodic
transition rates. We find that for both entropy production and heat
dissipation, the usual Gallavotti-Cohen symmetry of the rate function is
generalized to an analogous relation between the rate functions of the original
process and its corresponding backward process, in which the trajectory and the
driving protocol have been time-reversed. The effect is that spontaneous
positive fluctuations in the long time average of each quantity in the forward
process are exponentially more likely than spontaneous negative fluctuations in
the backward process, and vice-versa, revealing that the distributions of
fluctuations in universes in which time moves forward and backward are related.
As an additional result, the asymptotic time-averaged entropy production is
obtained as the integral of a periodic entropy production rate that generalizes
the constant rate pertaining to homogeneous dynamics
A relative entropy rate method for path space sensitivity analysis of stationary complex stochastic dynamics
We propose a new sensitivity analysis methodology for complex stochastic
dynamics based on the Relative Entropy Rate. The method becomes computationally
feasible at the stationary regime of the process and involves the calculation
of suitable observables in path space for the Relative Entropy Rate and the
corresponding Fisher Information Matrix. The stationary regime is crucial for
stochastic dynamics and here allows us to address the sensitivity analysis of
complex systems, including examples of processes with complex landscapes that
exhibit metastability, non-reversible systems from a statistical mechanics
perspective, and high-dimensional, spatially distributed models. All these
systems exhibit, typically non-gaussian stationary probability distributions,
while in the case of high-dimensionality, histograms are impossible to
construct directly. Our proposed methods bypass these challenges relying on the
direct Monte Carlo simulation of rigorously derived observables for the
Relative Entropy Rate and Fisher Information in path space rather than on the
stationary probability distribution itself. We demonstrate the capabilities of
the proposed methodology by focusing here on two classes of problems: (a)
Langevin particle systems with either reversible (gradient) or non-reversible
(non-gradient) forcing, highlighting the ability of the method to carry out
sensitivity analysis in non-equilibrium systems; and, (b) spatially extended
Kinetic Monte Carlo models, showing that the method can handle high-dimensional
problems
Dynamics of Polymers: a Mean-Field Theory
We derive a general mean-field theory of inhomogeneous polymer dynamics; a
theory whose form has been speculated and widely applied, but not heretofore
derived. Our approach involves a functional integral representation of a
Martin-Siggia-Rose type description of the exact many-chain dynamics. A saddle
point approximation to the generating functional, involving conditions where
the MSR action is stationary with respect to a collective density field
and a conjugate MSR response field , produces the desired dynamical
mean-field theory. Besides clarifying the proper structure of mean-field theory
out of equilibrium, our results have implications for numerical studies of
polymer dynamics involving hybrid particle-field simulation techniques such as
the single-chain in mean-field method (SCMF)
Beyond the Spectral Theorem: Spectrally Decomposing Arbitrary Functions of Nondiagonalizable Operators
Nonlinearities in finite dimensions can be linearized by projecting them into
infinite dimensions. Unfortunately, often the linear operator techniques that
one would then use simply fail since the operators cannot be diagonalized. This
curse is well known. It also occurs for finite-dimensional linear operators. We
circumvent it by developing a meromorphic functional calculus that can
decompose arbitrary functions of nondiagonalizable linear operators in terms of
their eigenvalues and projection operators. It extends the spectral theorem of
normal operators to a much wider class, including circumstances in which poles
and zeros of the function coincide with the operator spectrum. By allowing the
direct manipulation of individual eigenspaces of nonnormal and
nondiagonalizable operators, the new theory avoids spurious divergences. As
such, it yields novel insights and closed-form expressions across several areas
of physics in which nondiagonalizable dynamics are relevant, including
memoryful stochastic processes, open non unitary quantum systems, and
far-from-equilibrium thermodynamics.
The technical contributions include the first full treatment of arbitrary
powers of an operator. In particular, we show that the Drazin inverse,
previously only defined axiomatically, can be derived as the negative-one power
of singular operators within the meromorphic functional calculus and we give a
general method to construct it. We provide new formulae for constructing
projection operators and delineate the relations between projection operators,
eigenvectors, and generalized eigenvectors.
By way of illustrating its application, we explore several, rather distinct
examples.Comment: 29 pages, 4 figures, expanded historical citations;
http://csc.ucdavis.edu/~cmg/compmech/pubs/bst.ht
Transition times and stochastic resonance for multidimensional diffusions with time periodic drift: A large deviations approach
We consider potential type dynamical systems in finite dimensions with two
meta-stable states. They are subject to two sources of perturbation: a slow
external periodic perturbation of period and a small Gaussian random
perturbation of intensity , and, therefore, are mathematically
described as weakly time inhomogeneous diffusion processes. A system is in
stochastic resonance, provided the small noisy perturbation is tuned in such a
way that its random trajectories follow the exterior periodic motion in an
optimal fashion, that is, for some optimal intensity . The
physicists' favorite, measures of quality of periodic tuning--and thus
stochastic resonance--such as spectral power amplification or signal-to-noise
ratio, have proven to be defective. They are not robust w.r.t. effective model
reduction, that is, for the passage to a simplified finite state Markov chain
model reducing the dynamics to a pure jumping between the meta-stable states of
the original system. An entirely probabilistic notion of stochastic resonance
based on the transition dynamics between the domains of attraction of the
meta-stable states--and thus failing to suffer from this robustness defect--was
proposed before in the context of one-dimensional diffusions. It is
investigated for higher-dimensional systems here, by using extensions and
refinements of the Freidlin--Wentzell theory of large deviations for time
homogeneous diffusions. Large deviations principles developed for weakly time
inhomogeneous diffusions prove to be key tools for a treatment of the problem
of diffusion exit from a domain and thus for the approach of stochastic
resonance via transition probabilities between meta-stable sets.Comment: Published at http://dx.doi.org/10.1214/105051606000000385 in the
Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute
of Mathematical Statistics (http://www.imstat.org
Testing Homogeneity of Time-Continuous Rating Transitions
Banks could achieve substantial improvements of their portfolio credit risk assessment by estimating rating transition matrices within a time-continuous Markov model, thereby using continuous-time rating transitions provided by internal rating systems instead of discrete-time rating information. A non-parametric test for the hypothesis of time-homogeneity is developed. The alternative hypothesis is multiple structural change of transition intensities, i.e. time-varying transition probabilities. The partial-likelihood ratio for the multivariate counting process of rating transitions is shown to be asymptotically c2 -distributed. A Monte Carlo simulation finds both size and power to be adequate for our example. We analyze transitions in credit-ratings in a rating system with 8 rating states and 2743 transitions for 3699 obligors observed over seven years. The test rejects the homogeneity hypothesis at all conventional levels of significance. --Portfolio credit risk,Rating transitions,Markov model,time-homogeneity,partial likelihood
Statistics of randomly branched polymers in a semi-space
We investigate the statistical properties of a randomly branched
3--functional --link polymer chain without excluded volume, whose one point
is fixed at the distance from the impenetrable surface in a 3--dimensional
space. Exactly solving the Dyson-type equation for the partition function
in 3D, we find the "surface" critical
exponent , as well as the density profiles of 3--functional units
and of dead ends. Our approach enables to compute also the pairwise correlation
function of a randomly branched polymer in a 3D semi-space.Comment: 15 pages 7 figsures; section VII is slightly reorganized, discussion
is revise
Quantitative analysis of Clausius inequality
In the context of driven diffusive systems, for thermodynamic transformations
over a large but finite time window, we derive an expansion of the energy
balance. In particular, we characterize the transformations which minimize the
energy dissipation and describe the optimal correction to the quasi-static
limit. Surprisingly, in the case of transformations between homogeneous
equilibrium states of an ideal gas, the optimal transformation is a sequence of
inhomogeneous equilibrium states.Comment: arXiv admin note: text overlap with arXiv:1404.646
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