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Register a new userGrazing dynamics and dependence on initial conditions in certain systems with impacts
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Dynamics near the grazing manifold and basins of attraction for a motion of a material point in a gravitational field, colliding with a moving motion-limiting stop, are investigated. The Poincare map, describing evolution from an impact to the next impact, is derived. Periodic points are found and their stability is determined. The grazing manifold is computed and dynamics is approximated in its vicinity. It is shown that on the grazing manifold there are trapping as well as forbidden regions. Finally, basins of attraction are studied.
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This article discusses dependence on initial conditions in natural and social sciences with focus on physical science. The main focus is on the newly discovered rough dependence on initial data.
Dependence of the transient process duration on the initial conditions is considered in one- and two-dimensional systems with discrete time, representing a logistic map and the Eno map, respectively.
We set the formalism to study the way in which the choice of canonical equilibrium initial conditions affect the real-time dynamics of quantum disordered models. We use a path integral formulation on a time contour with real and imaginary time branches. The factorisation of the time-integration paths usually assumed in field-theoretical studies breaks down due to the averaging over quenched randomness. We derive the set of Schwinger-Dyson dynamical equations that govern the evolution of linear response and correlation functions. The solution of these equations is not straightforward as it needs, as an input, the full imaginary-time (or Matsubara frequency) dependence of the correlation in equilibrium. We check some limiting cases (equilibrium dynamics, classical limit) and we set the stage for the analytic and numerical analysis of quenches in random quantum systems.
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In this paper we study the problem of inferring the initial conditions of a dynamical system under incomplete information. Studying several model systems, we infer the latent microstates that best reproduce an observed time series when the observations are sparse,noisy and aggregated under a (possibly) nonlinear observation operator. This is done by minimizing the least-squares distance between the observed time series and a model-simulated time series using gradient-based methods. We validate this method for the Lorenz and Mackey-Glass systems by making out-of-sample predictions. Finally, we analyze the predicting power of our method as a function of the number of observations available. We find a critical transition for the Mackey-Glass system, beyond which it can be initialized with arbitrary precision.
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