Kernel machines have sustained continuous progress in the field of quantum chemistry. In
particular, they have proven to be successful in the low-data regime of force field …

Domain decomposition (DD) methods are widely used as preconditioner techniques. Their
effectiveness relies on the choice of a locally constructed coarse space. Thus far, this …

Human locomotion is an imperative topic to be conversed among researchers. Predicting
the human motion using multiple techniques and algorithms has always been a motivating …

We are interested in the Monte Carlo (MC) sampling of discretized elliptic partial differential
equations (PDEs) with random variable coefficients. The dominant computational load of …

This paper discusses parGeMSLR, a C++/MPI software library for the solution of sparse
systems of linear algebraic equations via preconditioned Krylov subspace methods in …

Low-rank matrix approximations appear in a number of scientific computing applications. We
consider the Nyström method for approximating a positive semidefinite matrix. In the case …

The simulation of fluid dynamic problems often involves solving large-scale saddle-point
systems. Their numerical solution with iterative solvers requires efficient preconditioners …

We develop and analyze low-rank updates for preconditioners that are based on a
(randomized) low-rank approximation of the error between the identity matrix and the …

Large sparse matrices arise in many applications in science and engineering, where the
solution of a linear system or an eigenvalue problem is needed. While direct methods are …

We are interested in the Monte Carlo (MC) sampling of discretized elliptic partial differential
equations (PDEs) with random variable coefficients. The dominant computational load of …