Dynamical low-rank approximation has become a valuable tool to perform an on-the-fly
model order reduction for prohibitively large matrix differential equations. A core ingredient …

Time-dependent kinetic models are ubiquitous in computational science and engineering.
The underlying integro-differential equations in these models are high-dimensional …

We propose a dynamical low-rank algorithm for a gyrokinetic model that is used to describe
strongly magnetized plasmas. The low-rank approximation is based on a decomposition into …

It is increasingly realized that taking stochastic effects into account is important in order to
study biological cells. However, the corresponding mathematical formulation, the chemical …

The stochastic description of chemical reaction networks with the kinetic chemical master
equation (CME) is important for studying biological cells, but it suffers from the curse of …

Computing the dominant eigenvalue is important in nuclear systems as it determines the
stability of the system (ie whether the system is sub or supercritical). Recently, the work of …

This paper introduces a novel computational approach termed the Reduced Augmentation
Implicit Low-rank (RAIL) method by investigating two predominant research directions in low …

Abstract The Swift–Hohenberg (SH) equation is an important model in the study of pattern
formation. In this paper, we propose two full-rank splitting schemes and a low-rank …

Abstract Motivation The Chemical Master Equation (CME) is a set of linear differential
equations that describes the evolution of the probability distribution on all possible …

Boolean reaction networks are an important tool in biochemistry for studying mechanisms in
the biological cell. However, the stochastic formulation of such networks requires the …