Search results for "Markov proce"
showing 10 items of 147 documents
Generalized Langevin dynamics: construction and numerical integration of non-Markovian particle-based models.
2018
We propose a generalized Langevin dynamics (GLD) technique to construct non-Markovian particle-based coarse-grained models from fine-grained reference simulations and to efficiently integrate them. The proposed GLD model has the form of a discretized generalized Langevin equation with distance-dependent two-particle contributions to the self- and pair-memory kernels. The memory kernels are iteratively reconstructed from the dynamical correlation functions of an underlying fine-grained system. We develop a simulation algorithm for this class of non-Markovian models that scales linearly with the number of coarse-grained particles. Our GLD method is suitable for coarse-grained studies of syste…
Reconstruction of time-dependent coefficients: a check of approximation schemes for non-Markovian convolutionless dissipative generators
2010
We propose a procedure to fully reconstruct the time-dependent coefficients of convolutionless non-Markovian dissipative generators via a finite number of experimental measurements. By combining a tomography based approach with a proper data sampling, our proposal allows to relate the time-dependent coefficients governing the dissipative evolution of a quantum system to experimentally accessible quantities. The proposed scheme not only provides a way to retrieve full information about potentially unknown dissipative coefficients but also, most valuably, can be employed as a reliable consistency test for the approximations involved in the theoretical derivation of a given non-Markovian convo…
Correction: Generalized Langevin dynamics: construction and numerical integration of non-Markovian particle-based models.
2018
Correction for ‘Generalized Langevin dynamics: construction and numerical integration of non-Markovian particle-based models’ by Gerhard Jung et al., Soft Matter, 2018, DOI: 10.1039/c8sm01817k.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
2016
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…
The use of Markovian metapopulation models: a comparison of three methods reducing the dimensionality of transition matrices.
2001
The use of Markovian models is an established way for deriving the complete distribution of the size of a population and the probability of extinction. However, computationally impractical transition matrices frequently result if this mathematical approach is applied to natural populations. Binning, or aggregating population sizes, has been used to permit a reduction in the dimensionality of matrices. Here, we present three deterministic binning methods and study the errors due to binning for a metapopulation model. Our results indicate that estimation errors of the investigated methods are not consistent and one cannot make generalizations about the quality of a method. For some compared o…
A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton
2019
Author's accepted version (post-print). © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Available from 20/03/2021. This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probabl…
Bismut’s Way of the Malliavin Calculus for Non-Markovian Semi-groups: An Introduction
2019
We give a review of our recent works related to the Malliavin calculus of Bismut type for non-Markovian generators. Part IV is new and relates the Malliavin calculus and the general theory of elliptic pseudo-differential operators.
Fluctuation theorems for non-Markovian quantum processes
2013
Exploiting previous results on Markovian dynamics and fluctuation theorems, we study the consequences of memory effects on single realizations of nonequilibrium processes within an open system approach. The entropy production along single trajectories for forward and backward processes is obtained with the help of a recently proposed classical-like non-Markovian stochastic unravelling, which is demonstrated to lead to a correction of the standard entropic fluctuation theorem. This correction is interpreted as resulting from the interplay between the information extracted from the system through measurements and the flow of information from the environment to the open system: Due to memory e…
Competition between memory-keeping and memory-erasing decoherence channels
2014
We study the competing effects of simultaneous Markovian and non-Markovian decoherence mechanisms acting on a single spin. We show the existence of a threshold in the relative strength of such mechanisms above which the spin dynamics becomes fully Markovian, as revealed by the use of several non-Markovianity measures. We identify a measure-dependent nested structure of such thresholds, hinting at a causality relationship among the various non-Markovianity witnesses used in our analysis. Our considerations are then used to argue the unavoidably non-Markovian evolution of a single-electron quantum dot exposed to both intrinsic and Markovian technical noise, the latter of arbitrary strength.
A Joint Time-Space Domain Analysis for Ultra-Reliable Communication in 5G Networks
2018
Reliable communication is a fundamental service level agreement criterion in wireless networks. Moreover, it is a performance indicator for ultra- reliable communication (URC) in emerging 5G networks. In URC, the quality of the delivered services to end-users ultimately depends on the availability of network resources as well as the quality of the received signal. Therefore, in order to analyze URC in cellular networks, both time- dependent spectrum availability and space-dependent service quality need to be considered. In this paper, we present a combined time and space domain analysis on network reliability based on a continuous time Markov chain model. To this end, we introduce a new rel…