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We present a comprehensive study of Vicsek-style self-propelled particle models in two and three space dimensions. The onset of collective motion in such stochastic models with only local alignment interactions is studied in detail and…

Statistical Mechanics · Physics 2009-11-13 Hugues Chaté , Francesco Ginelli , Guillaume Grégoire , Franck Raynaud

In classical physics, memoryless dynamics and Markovian statistics are one and the same. This is not true for quantum dynamics, first and foremost because quantum measurements are invasive. Going beyond measurement invasiveness, here we…

Quantum Physics · Physics 2023-05-02 Philip Taranto , Thomas J. Elliott , Simon Milz

We study the evolution of the dynamics across a generic first order quantum phase transition in an interacting boson model of nuclei. The dynamics inside the phase coexistence region exhibits a very simple pattern. A classical analysis…

Nuclear Theory · Physics 2015-03-13 A. Leviatan , M. Macek

Predictive equivalence in discrete stochastic processes have been applied with great success to identify randomness and structure in statistical physics and chaotic dynamical systems and to inferring hidden Markov models. We examine the…

Statistical Mechanics · Physics 2021-09-21 Samuel P. Loomis , James P. Crutchfield

The classical Floquet theory allows to map a time-periodic system of linear differential equations into an autonomous one. By looking at it in a geometrical way, we extend the theory to a class of non-autonomous non-periodic equations. This…

Mathematical Physics · Physics 2025-10-01 Giuseppe Gaeta , Sebastian Walcher

In this paper, we introduce the concept of random time changes in dynamical systems. The sub- ordination principle may be applied to study the long time behavior of the random time systems. We show, under certain assumptions on the class of…

Dynamical Systems · Mathematics 2021-08-21 R. Capuani , L. Di Persio , Y. Kondratiev , M. Ricciardi , J. L. da Silva

Many reinforcement learning algorithms are built on an assumption that an agent interacts with an environment over fixed-duration, discrete time steps. However, physical systems are continuous in time, requiring a choice of…

Machine Learning · Computer Science 2024-09-04 Kris De Asis , Richard S. Sutton

We introduce a family of discrete dynamical systems which includes, and generalizes, the mutation dynamics of rank two cluster algebras. These systems exhibit behavior associated with integrability, namely preservation of a symplectic form,…

Dynamical Systems · Mathematics 2023-04-28 John Machacek , Nicholas Ovenhouse

A temporally varying discretization often features in discrete gravitational systems and appears in lattice field theory models subject to a coarse graining or refining dynamics. To better understand such discretization changing dynamics in…

General Relativity and Quantum Cosmology · Physics 2014-10-03 Philipp A. Hoehn

We study classical stochastic systems with discrete states, coupled to switching external environments. For fast environmental processes we derive reduced dynamics for the system itself, focusing on corrections to the adiabatic limit of…

Statistical Mechanics · Physics 2019-03-27 Peter G. Hufton , Yen Ting Lin , Tobias Galla

Sticky diffusion models a Markovian particle experiencing reflection and temporary adhesion phenomena at the boundary. Numerous numerical schemes exist for approximating stopped or reflected stochastic differential equations (SDEs), but…

Numerical Analysis · Mathematics 2025-08-11 Akash Sharma

Time-space noncommutativity leads to quantisation of time and energy nonconservation when time is conjugate to a compact spatial direction like a circle. In this context energy is conserved only modulo some fixed unit. Such a possibility…

High Energy Physics - Theory · Physics 2010-10-27 A. P. Balachandran , A. G. Martins , P. Teotonio-Sobrinho

The mathematical model of a linear system with the short memory about own stochastic behavior is proposed. It is assumed that the system is under a continual influence of independent stochastic impulses. In a short memory approximation the…

Probability · Mathematics 2008-12-10 D. N. Zhabin

Memory is the fundamental form of temporal complexity: when present but uncontrollable, it manifests as non-Markovian noise; conversely, if controllable, memory can be a powerful resource for information processing. Memory effects arise…

Quantum Physics · Physics 2024-05-07 Philip Taranto , Marco Túlio Quintino , Mio Murao , Simon Milz

The synapses of real neural systems seem to have delays. Therefore, it is worthwhile to analyze associative memory models with delayed synapses. Thus, a sequential associative memory model with delayed synapses is discussed, where a…

Disordered Systems and Neural Networks · Physics 2007-05-23 Seiji Miyoshi , Hiro-Fumi Yanai , Masato Okada

We consider the evolution of correlation functions in a non-Markov version of the contact model in the continuum. The memory effects are introduced by assuming the fractional evolution equation for the statistical dynamics. This leads to a…

Mathematical Physics · Physics 2014-12-02 Anatoly N. Kochubei , Yuri G. Kondratiev

In this paper we demonstrate that Lindblad equations characterized by a random rate variable arise after tracing out a complex structured reservoir. Our results follows from a generalization of the Born-Markov approximation, which relies in…

Quantum Physics · Physics 2009-11-11 Adrian A. Budini

There is a tendency to write the equations of general relativity as a first order symmetric system of time dependent partial differential equations. However, for numerical reasons, it might be advantageous to use a second order formulation…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Heinz-O. Kreiss , Omar E. Ortiz

We develop reduced, stochastic models for high dimensional, dissipative dynamical systems that relax very slowly to equilibrium and can encode long term memory. We present a variety of empirical and first principles approaches for model…

Statistical Mechanics · Physics 2017-05-24 Shankar C. Venkataramani , Raman C. Venkataramani , Juan M. Restrepo

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes. A special case of wide interest…

Machine Learning · Computer Science 2016-08-30 Hossein Hosseini , Sreeram Kannan , Baosen Zhang , Radha Poovendran
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