Abstract. In this paper we propose a new technique for verification by simulation of continuous and hybrid dynamical systems with uncertain initial conditions. We provide an algorithmic methodology that can, in most cases, verify that the system avoids a set of bad states by conducting a finite number of simulation runs starting from a finite subset of the set of possible initial conditions. The novelty of our approach consists in the use of sensitivity analysis, developed and implemented in the context of numerical integration, to efficiently characterize the coverage of sampling trajectories.
This contribution presents an overview of sensitivity analysis of simulation models, including the e...
An important criticism of traditional methods of inverse simulation that are based on the Newton–Rap...
International audienceWe are interested in the sensitivity analysis of numerical simulators in the c...
We consider linear dynamical systems including random parameters for uncertainty quantification. A s...
Discrete-event stochastic simulation is a powerful tool for understanding and evaluating complex sys...
Control strategies for nonlinear dynamical systems often make use of special system properties, whic...
Sensitivity analysis studies how the variation in the numerical output of a model can be quantitativ...
Bootstrap resampling is an extremely practical and effective way of studying the distributional prop...
Sensitivity analysis can be used to quantify the magnitude of the dependency of model predictions on...
AbstractUncertainty quantification techniques are increasingly important in the interpretation of da...
We will use the word simulation exclusively for the technique to mimic a random process on a compute...
The solution of several operations research problems requires the creation of a quantitative model. ...
An important criticism of traditional methods of inverse simulation that are based on the Newton–Rap...
The development of practical numerical methods for simulation of partial differential equations lead...
Verification and validation (V&V) are playing more important roles to quantify uncertainties and...
This contribution presents an overview of sensitivity analysis of simulation models, including the e...
An important criticism of traditional methods of inverse simulation that are based on the Newton–Rap...
International audienceWe are interested in the sensitivity analysis of numerical simulators in the c...
We consider linear dynamical systems including random parameters for uncertainty quantification. A s...
Discrete-event stochastic simulation is a powerful tool for understanding and evaluating complex sys...
Control strategies for nonlinear dynamical systems often make use of special system properties, whic...
Sensitivity analysis studies how the variation in the numerical output of a model can be quantitativ...
Bootstrap resampling is an extremely practical and effective way of studying the distributional prop...
Sensitivity analysis can be used to quantify the magnitude of the dependency of model predictions on...
AbstractUncertainty quantification techniques are increasingly important in the interpretation of da...
We will use the word simulation exclusively for the technique to mimic a random process on a compute...
The solution of several operations research problems requires the creation of a quantitative model. ...
An important criticism of traditional methods of inverse simulation that are based on the Newton–Rap...
The development of practical numerical methods for simulation of partial differential equations lead...
Verification and validation (V&V) are playing more important roles to quantify uncertainties and...
This contribution presents an overview of sensitivity analysis of simulation models, including the e...
An important criticism of traditional methods of inverse simulation that are based on the Newton–Rap...
International audienceWe are interested in the sensitivity analysis of numerical simulators in the c...