On the stochastic representation and Markov approximation of Hamiltonian systems

DOI10.1063/5.0001435zbMath1451.82024OpenAlexW3047581572WikidataQ98886418 ScholiaQ98886418MaRDI QIDQ5129855

Bernard Gaveau, Michel Moreau

Publication date: 2 November 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1063/5.0001435

zbMATH Keywords

Hamiltonian systemstochastic descriptionMarkov equation

Mathematics Subject Classification ID

Stationary stochastic processes (60G10) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Classical dynamic and nonequilibrium statistical mechanics (general) (82C05)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A Mathematical Theory of Communication
• Fluctuation theorem for currents and Schnakenberg network theory
• Entropy dissipation rate and convergence in kinetic equations
• Ergodicity, ensembles, irreversibility in Boltzmann and beyond.
• Dynamical ensembles in stationary states.
• Creation, dissipation and recycling of resources in non-equilibrium systems
• Note on Paley-Wiener's theorem
• Probability of second law violations in shearing steady states
• A note on the Jarzynski equality
• Information Theory and Statistical Mechanics
• Fluctuation theorem for stochastic dynamics
• Ergodic theory of chaos and strange attractors
• An Introduction to Chaos in Nonequilibrium Statistical Mechanics
• Fluctuations in nonequilibrium statistical mechanics: models, mathematical theory, physical mechanisms
• Probabilistic ideas in ergodic theory
• The Basic Theorems of Information Theory

This page was built for publication: On the stochastic representation and Markov approximation of Hamiltonian systems