4 citations · 4 across the 3 of their papers we have counts for
4 papers
A Probabilistic Approach to Shape Derivatives
Luka Schlegel, Volker Schulz, Frank T. Seifried +1
We introduce a novel mesh-free and direct method for computing the shape derivative in PDE-constrained shape optimization problems. Our approach is based on a probabilistic represe…
Shape optimization in the space of piecewise-smooth shapes for the Bingham flow variational inequality
Tim Suchan, Volker Schulz, Kathrin Welker
This paper sets up an approach for shape optimization problems constrained by variational inequalities (VI) in an appropriate shape space. In contrast to classical VI, where no exp…
Riemannian Optimization for Variance Estimation in Linear Mixed Models
Lena Sembach, Jan Pablo Burgard, Volker H. Schulz
Variance parameter estimation in linear mixed models is a challenge for many classical nonlinear optimization algorithms due to the positive-definiteness constraint of the random e…
A Riemannian Newton Trust-Region Method for Fitting Gaussian Mixture Models
Lena Sembach, Jan Pablo Burgard, Volker H. Schulz
Gaussian Mixture Models are a powerful tool in Data Science and Statistics that are mainly used for clustering and density approximation. The task of estimating the model parameter…