This paper introduces three types of dynamical indicators that capture the effect of uncertainty on the time evolution of dynamical systems. Two indicators are derived from the definition of Finite Time Lyapunov Exponents while a third indicator directly exploits the property of the polynomial expansion of the dynamics with respect to the uncertain quantities. The paper presents the derivation of the indicators and a number of numerical experiments that illustrates the use of these indicators to depict a cartography of the phase space under parametric uncertainty and to identify robust initial conditions and regions of practical stability in the restricted three-body problem
In the last decades finite time chaos indicators have been used to compute the phase-portraits of co...
A new technique for calculating the time-evolution, correlations and steady state spectra for nonlin...
To describe physical problems we often make use of deterministic mathematical models. Typical consti...
This study applies generalized polynomial chaos theory to dynamic systems with uncertainties
International audienceIn this paper, we propose polynomial forms to represent distributions of state...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
Time delay is ubiquitous in many real-world physical and biological systems. It typically gives rise...
A method is presented to estimate the region of attraction (ROA) of stochastic systems with finite s...
Concepts and measures of time series uncertainty and complexity have been applied across domains for...
In this work, two novel dynamics indicators are introduced and used to characterise the uncertain dy...
ABSTRACT: Uncertainty quantification is the state-of-the-art framework dealing with uncertainties ar...
In this article, we develop a set-oriented numerical methodology which allows us to perform uncertai...
We consider linear dynamical systems including random parameters for uncertainty quantification. A s...
An adaptative phase-space discretization strategy for the global analysis of stochastic nonlinear dy...
In the last decades finite time chaos indicators have been used to compute the phase-portraits of co...
A new technique for calculating the time-evolution, correlations and steady state spectra for nonlin...
To describe physical problems we often make use of deterministic mathematical models. Typical consti...
This study applies generalized polynomial chaos theory to dynamic systems with uncertainties
International audienceIn this paper, we propose polynomial forms to represent distributions of state...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
Time delay is ubiquitous in many real-world physical and biological systems. It typically gives rise...
A method is presented to estimate the region of attraction (ROA) of stochastic systems with finite s...
Concepts and measures of time series uncertainty and complexity have been applied across domains for...
In this work, two novel dynamics indicators are introduced and used to characterise the uncertain dy...
ABSTRACT: Uncertainty quantification is the state-of-the-art framework dealing with uncertainties ar...
In this article, we develop a set-oriented numerical methodology which allows us to perform uncertai...
We consider linear dynamical systems including random parameters for uncertainty quantification. A s...
An adaptative phase-space discretization strategy for the global analysis of stochastic nonlinear dy...
In the last decades finite time chaos indicators have been used to compute the phase-portraits of co...
A new technique for calculating the time-evolution, correlations and steady state spectra for nonlin...
To describe physical problems we often make use of deterministic mathematical models. Typical consti...