Jump-Drift and Jump-Diffusion Processes : Large Deviations for the density, the current and the jump-flow and for the excursions between jumps
arXiv:2104.10392 · doi:10.1088/1742-5468/ac12c5
Abstract
For one-dimensional Jump-Drift and Jump-Diffusion processes converging towards some steady state, the large deviations of a long dynamical trajectory are described from two perspectives. Firstly, the joint probability of the empirical time-averaged density, of the empirical time-averaged current and of the empirical time-averaged jump-flow are studied via the large deviations at Level 2.5. Secondly, the joint probability of the empirical jumps and of the empirical excursions between consecutive jumps are analyzed via the large deviations at Level 2.5 for the alternate Markov chain that governs the series of all the jump events of a long trajectory. These two general frameworks are then applied to three examples of positive jump-drift processes without diffusion, and to two examples of jump-diffusion processes, in order to illustrate various simplifications that may occur in rate functions and in contraction procedures.
29 pages
References in corpus (11)
- The large deviation approach to statistical mechanics
- Non equilibrium steady states: fluctuations and large deviations of the density and of the current
- Dynamic first-order phase transition in kinetically constrained models of glasses
- First-order dynamical phase transition in models of glasses: an approach based on ensembles of histories
- Steady state statistics of driven diffusions
- Construction of a Coordinate Bethe Ansatz for the asymmetric simple exclusion process with open boundaries
- A minimal model of dynamical phase transition
- Dynamical large deviations of reflected diffusions
- Thermodynamic formalism and large deviation functions in continuous time Markov dynamics
- Role of current fluctuations in nonreversible samplers
- Revisiting the Ruelle thermodynamic formalism for Markov trajectories with application to the glassy phase of random trap models
Cited by in corpus (16)
- Large deviations at various levels for run-and-tumble processes with space-dependent velocities and space-dependent switching rates
- Large deviations for the Skew-Detailed-Balance Lifted-Markov processes to sample the equilibrium distribution of the Curie-Weiss model
- Anomalous dynamical large deviations of local empirical densities and activities in the pure and in the random kinetically-constrained East Model
- Large deviations for metastable states of Markov processes with absorbing states with applications to population models in stable or randomly switching environment
- Conditioned diffusion processes with an absorbing boundary condition for finite or infinite horizon
- Conditioning diffusion processes with killing rates
- Microcanonical conditioning of Markov processes on time-additive observables
- Revisiting boundary-driven non-equilibrium Markov dynamics in arbitrary potentials via supersymmetric quantum mechanics and explicit large deviations at various levels
- Markov trajectories : Microcanonical Ensembles based on empirical observables as compared to Canonical Ensembles based on Markov generators
- Inhomogeneous asymmetric exclusion processes between two reservoirs : large deviations for the local empirical observables in the Mean-Field approximation
- Explicit dynamical properties of the Pelikan random map in the chaotic region and at the intermittent critical point towards the non-chaotic region
- Large deviations at level 2.5 and for trajectories observables of diffusion processes : the missing parts with respect to their random-walks counterparts
- Large deviations for trajectory observables of diffusion processes in dimension in the double limit of large time and small diffusion coefficient
- A supersymmetric quantum perspective on the explicit large deviations for reversible Markov jump processes, with applications to pure and random spin chains
- Inverse problem in the conditioning of Markov processes on trajectory observables : what canonical conditionings can connect two given Markov generators ?
- Conditioning two diffusion processes with respect to their first-encounter properties