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We present a bivariate vector valued discrete autoregressive model of order $1$ (BDAR($1$)) for discrete time series. The BDAR($1$) model assumes that each time series follows its own univariate DAR($1$) model with dependent random…

Methodology · Statistics 2025-10-08 Anna Nalpantidi , Dimitris Karlis

A new model for the drag coefficient of a sphere in a concentrated system is described. It is based upon a cell-averaged model for the Stokes regime combined with a physically motivated extrapolation to arbitrary Reynolds number. It can be…

Fluid Dynamics · Physics 2015-01-13 Gary Tupper , Indresan Govender , Aubrey Mainza

The development of efficient and robust dynamic models is fundamental in the field of systems and control engineering. In this paper, a new formulation for the dynamic model of nonlinear mechanical systems, that can be applied to different…

Systems and Control · Electrical Eng. & Systems 2026-02-09 Davide Tebaldi , Roberto Zanasi

We analyze the asymptotic behavior of sequences of random variables defined by an initial condition, a stationary and ergodic sequence of random matrices, and an induction formula involving multiplication is the so-called max-plus algebra.…

Probability · Mathematics 2008-03-12 Glenn Merlet

In this paper, we consider a class of stochastic impulse control problem when there is a fixed delay $\Delta$ between the decision and execution times. The dynamics of the controlled system between two impulses is an arbitrary adapted…

Probability · Mathematics 2026-01-23 Said Hamadène , Ibtissam Hdhiri

Proper modeling of complex systems requires innovative mathematical tools. In this sense, we sought to use deformed or fractal derivatives for studying the dynamics of systems, particularly those, such as granular gases, in which the…

Statistical Mechanics · Physics 2024-06-27 José Weberszpil , Cresus F. de L. Godinho , Ion Vasile Vancea

A control design approach is developed for a general class of uncertain strict-feedback-like nonlinear systems with dynamic uncertain input nonlinearities with time delays. The system structure considered in this paper includes a nominal…

Optimization and Control · Mathematics 2018-01-09 Prashanth Krishnamurthy , Farshad Khorrami

In this paper, we consider the relationship between phase-type distributions and positive systems through practical examples. Phase-type distributions, commonly used in modelling dynamic systems, represent the temporal evolution of a set of…

Methodology · Statistics 2024-08-20 Luz Judith Rodríguez Esparza , Fernando Baltazar Larios

We conjecture that for a wide class of interacting particle systems evolving in discrete time, namely conservative cellular automata with piecewise linear flow diagram, relaxation to the limit set follows the same power law at critical…

Cellular Automata and Lattice Gases · Physics 2009-11-07 Henryk Fuks , Nino Boccara

Current dynamical control based on the bang-bang control mechanism involving various types of pulse sequences is essentially a perturbative theory. This paper presents a non-perturbative dynamical control approach based on the exact…

Quantum Physics · Physics 2013-09-03 Jun Jing , Lian-Ao Wu , J. Q. You , Ting Yu

We present a time-dependent perturbative approach adapted to the treatment of intense pulsed interactions. We show there is a freedom in choosing secular terms and use it to optimize the accuracy of the approximation. We apply this…

Quantum Physics · Physics 2007-05-23 D. Daems , S. Guérin , H. R. Jauslin , A. Keller , O. Atabek

We develop an effective description of noise-induced oscillations based on deterministic phase dynamics. The phase equation is constructed to exhibit correct frequency and distribution density of noise-induced oscillations. In the simplest…

Chaotic Dynamics · Physics 2010-10-04 Justus T. C. Schwabedal , Arkady Pikovsky

In order to develop systems capable of artificial evolution, we need to identify which systems can produce complex behavior. We present a novel classification method applicable to any class of deterministic discrete space and time dynamical…

Artificial Intelligence · Computer Science 2021-08-04 Barbora Hudcová , Tomáš Mikolov

We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as…

Artificial Intelligence · Computer Science 2026-05-12 Joohyung Lee , Yi Wang

We report on self-induced switchings between multiple distinct space--time patterns in the dynamics of a spatially extended excitable system. These switchings between low-amplitude oscillations, nonlinear waves, and extreme events strongly…

Chaotic Dynamics · Physics 2016-03-21 Gerrit Ansmann , Klaus Lehnertz , Ulrike Feudel

The dynamics of time-dependent coupled oscillator model for the charged particle motion subjected to a time-dependent external magnetic field is investigated. We used canonical transformation approach for the classical treatment of the…

Quantum Physics · Physics 2015-05-20 Salah Menouar , Mustapha Maamache , Jeong Ryeol Choi

This paper introduces a compositional framework for constructing finite abstractions of nonlinear interconnected impulsive systems using dissipativity-based conditions. Central to our approach is the concept of "alternating simulation…

Systems and Control · Electrical Eng. & Systems 2024-12-24 Abdalla Swikir

We present an implementation of a new method for explicit simulations of time-dependent electric currents through nanojunctions. The method is based on unitary propagation of stroboscopic wave packet states and is designed to treat open…

Materials Science · Physics 2014-05-06 M. Konôpka , P. Bokes

Selected results for the stability and optimal control of abstract switched systems in Banach and Hilbert space are reviewed. The dynamics are typically given in a piecewise sense by a family of nonlinearly perturbed evolutions of strongly…

Optimization and Control · Mathematics 2018-02-23 Falk M. Hante

The identification of Linear Time-Varying (LTV) systems from input-output data is a fundamental yet challenging ill-posed inverse problem. This work introduces a unified Bayesian framework that models the system's impulse response, $h(t,…

Machine Learning · Statistics 2026-04-01 Yaniv Shulman