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We analyze the numerical performance of a preconditioning technique recently proposed in [1] for the efficient solution of parametrized linear systems arising from the finite element (FE) discretization of parameter-dependent elliptic partial differential ...
We present an application in multi-parametrized sub-domains based on a technique for the rapid and reliable prediction of linear-functional outputs of elliptic coercive partial differential equations with affine parameter dependence (reduced-basis methods) ...
Fibre-reinforced composites are being increasingly used as alternatives for conventional materials primarily because of their high strength, specific stiffness, light weight and adjustable properties. However, before using this type of material with confid ...
Soil moisture is crucial to water-cycle as it affects infiltration, runoff and land-atmosphere interactions. In dry soils the role of water vapor fluxes becomes essential and causes a tight coupling between soil moisture dynamics and heat transfer, which m ...
A priori and a posteriori error estimates are presented for nonlinear diffusion-convection problems when using the classical Streamline Upwind Petrov-Galerkin (SUPG) scheme and numerical integration. For this purpose, an abstract framework is developed. A ...
This work focuses on the security of critical infrastructures against time-synchronization attacks (TSA). A TSA can impact any network that relies on the dynamic analysis of data, by altering the time synchronization between its nodes. Such attacked networ ...
We analyze the numerical performance of a preconditioning technique recently proposed in [4] for the ecient solution of parametrized linear systems arising from the finite element (FE) discretization of parameter-dependent elliptic partial differential equ ...
Composite materials undoubtedly belong to one of the most studied classes of materials nowadays. In order to model the behaviour of mechanical structures made of this type of materials, it is necessary to know their constitutive properties. The varieties o ...
For a high dimensional problem, a randomized Gram-Schmidt (RGS) algorithm is beneficial in computational costs as well as numerical stability. We apply this dimension reduction technique by random sketching to Krylov subspace methods, e.g. to the generaliz ...
Commonly employed reconstruction algorithms in compressed sensing (CS) use the L-2 norm as the metric for the residual error. However, it is well-known that least squares (LS) based estimators are highly sensitive to outliers present in the measurement vec ...
Institute of Electrical and Electronics Engineers2013
