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method) for the time discretization of an unstationary Stokes problem in D c Rd (d = 2,3) given t, f,u0; (P) find (u, p) solution to ult. = u0, ujav = 0 and: au ptAu + V p = f, divu = 0 on (0, T) x D. at Inspired by the analyses of the Backward Euler schem ...
In this paper, a case study on the estimations of extreme floods is described. The watershed chosen for the analysis is the catchment of the Limmernboden dam situated in Switzerland. Statistical methods and the simulation based "Probable Maximum Precipitat ...
Isogeometric Analysis (IGA) is a computational methodology for the numerical approximation of Partial Differential Equations (PDEs). IGA is based on the isogeometric concept, for which the same basis functions, usually Non-Uniform Rational B-Splines (NURBS ...
In this paper, we present work on learning analytics that aims to support learners and teachers through dashboard applications, ranging from small mobile applications to learnscapes on large public displays. Dashboards typically capture and visualize trace ...
The identification of kinetic models is an important step for the monitoring, control and optimization of industrial processes. This is particularly the case for highly competitive business sectors such as chemical and pharmaceutical industries, where the ...
The convergence and efficiency of the reduced basis method used for the approximation of the solutions to a class of problems written as a parametrized PDE depends heavily on the choice of the elements that constitute the "reduced basis". The purpose of th ...
In this paper we derive regularity results for equilibria of multilattices under an external force and prove a priori and a posteriori error estimates for a multiscale numerical method for computing such equilibria. The estimates are derived in a W-1,W-inf ...
This paper presents a new method for the solution of multiscale stochastic differential equations at the diffusive time scale. In contrast to averaging-based methods, e.g., the heterogeneous multiscale method (HMM) or the equation-free method, which rely o ...
Machine learning algorithms are widely used today for analytical tasks such as data cleaning, data categorization, or data filtering. At the same time, the rise of social media motivates recent uptake in large scale graph processing. Both categories of alg ...
The objective of this work is to develop a numerical framework to perform rapid and reliable simulations for solving parametric problems in domains represented by networks and to extend the classical reduced basis method. Aimed at this scope, we propose tw ...