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20212024
most citedA parallel rank-adaptive integrator for dynamical low-rank approximation

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

math.NA2024

A multi-fidelity adaptive dynamical low-rank based optimization algorithm for fission criticality problems

C. Scalone, L. Einkemmer, J. Kusch +1

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

math.NA20242 cited

Second-order robust parallel integrators for dynamical low-rank approximation

Jonas Kusch

Due to its reduced memory and computational demands, dynamical low-rank approximation (DLRA) has sparked significant interest in multiple research communities. A central challenge…

math.NA2024

A robust second-order low-rank BUG integrator based on the midpoint rule

Gianluca Ceruti, Lukas Einkemmer, Jonas Kusch +1

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…

math.NA20232 cited

A parallel rank-adaptive integrator for dynamical low-rank approximation

Gianluca Ceruti, Jonas Kusch, Christian Lubich

This work introduces a parallel and rank-adaptive matrix integrator for dynamical low-rank approximation. The method is related to the previously proposed rank-adaptive basis updat…

physics.comp-ph2021

A flux reconstruction stochastic Galerkin scheme for hyperbolic conservation laws

Tianbai Xiao, Jonas Kusch, Julian Koellermeier +1

The study of uncertainty propagation poses a great challenge to design numerical solvers with high fidelity. Based on the stochastic Galerkin formulation, this paper addresses the…