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Learning from time-dependent streaming data with online stochastic algorithms

We study stochastic algorithms in a streaming framework, trained on samples coming from a dependent data source. In this streaming framework, we analyze the convergence of Stochastic Gradient (SG) methods in a non-asymptotic manner; this includes various SG methods such as the well-known stochastic gradient descent (i.e., Robbins-Monro algorithm), ...

Interactive blood damage analysis for ventricular assist devices

Interactive visualisation techniques for large time-dependent data sets

The research described in this thesis was part of a larger research project about multi-phase flows. These flows are characterised by a sharp transition between the fluids, the so-called phase front. One of the goals of the project was to study the evolution of the phase fronts using CFD, i.e. to study the development of the surfaces over time and ...