Bastian Bohn

TL;DR: This work proposes using methods from machine learning to semi-automatically analyze the arising finite element data and thereby significantly assist in the overall engineering process, and combines clustering and nonlinear dimensionality reduction to show that the method is able to automatically detect parameter dependent structure instabilities or reveal bifurcations in the time-dependent behavior of beams.

TL;DR: This work discusses explainable machine learning approaches which are used to tackle common challenges in the bio- and geosciences, such as limited amount of labeled data or the provision of reliable and scientific consistent results.

TL;DR: In this paper, a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces is presented.

TL;DR: A case-control study in idiopathic generalized epilepsy demonstrates that a full Genome-Wide Interaction Analyses (GWIA) is feasible through use of data compression, specific data representation, interleaved data organization, and parallelization of the analysis on a multi-processor system.