Generalized polynomial chaos (gPC) expansions are a powerful tool for studying differential equations with random coefficients, allowing, in particular, one to efficiently approximate random invariant sets associated with such equations. In this work, we use ideas from validated numerics in order to obtain rigorous a posteriori error estimates together with existence results about gPC expansions of random invariant sets. This approach also provides a new framework for conducting validated continuation, i.e., for rigorously computing isolated branches of solutions in parameter-dependent systems, which generalizes in a straightforward way to multiparameter continuation. We illustrate the proposed methodology by rigorously computing random inv...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
To describe physical problems we often make use of deterministic mathematical models. Typical consti...
International audienceDifferential equations with random parameters have gained significant prominen...
Abstract. In this paper we review some applications of generalized polynomial chaos expansion for un...
In many fields, active research is currently focused on quantification and simulation of model uncer...
A number of approaches for discretizing partial differential equations with random data ar...
This paper presents a methodology to quantify computationally the uncertainty in a class of differen...
The generalized Polynomial Chaos Expansion Method (gPCEM), which is a random uncertainty analysis me...
This thesis deals with two problems arising in the application of polynomial chaos (PC) in dynamical...
The first two moments of the steady-state response of a dynamical random system are determined throu...
Polynomial chaos (PC) expansions are used for the propagation of uncertainty through dynamical syste...
allows analyses of systems with low input correlations without suffering its restriction ty acr ende...
The first two moments of the steady-state response of a dynamical random system are determined throu...
Inherent physical uncertainties can have a significant influence on computational predictions. It is...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
To describe physical problems we often make use of deterministic mathematical models. Typical consti...
International audienceDifferential equations with random parameters have gained significant prominen...
Abstract. In this paper we review some applications of generalized polynomial chaos expansion for un...
In many fields, active research is currently focused on quantification and simulation of model uncer...
A number of approaches for discretizing partial differential equations with random data ar...
This paper presents a methodology to quantify computationally the uncertainty in a class of differen...
The generalized Polynomial Chaos Expansion Method (gPCEM), which is a random uncertainty analysis me...
This thesis deals with two problems arising in the application of polynomial chaos (PC) in dynamical...
The first two moments of the steady-state response of a dynamical random system are determined throu...
Polynomial chaos (PC) expansions are used for the propagation of uncertainty through dynamical syste...
allows analyses of systems with low input correlations without suffering its restriction ty acr ende...
The first two moments of the steady-state response of a dynamical random system are determined throu...
Inherent physical uncertainties can have a significant influence on computational predictions. It is...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
To describe physical problems we often make use of deterministic mathematical models. Typical consti...