Julian Roth

Julian Roth
Leibniz Universität Hannover · Institute of Applied Mathematics

Master of Science

About

5
Publications
368
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
8
Citations
Citations since 2017
5 Research Items
8 Citations
2017201820192020202120222023012345
2017201820192020202120222023012345
2017201820192020202120222023012345
2017201820192020202120222023012345
Education
October 2020 - August 2021
Leibniz Universität Hannover
Field of study
  • Mathematics
October 2017 - July 2020
Leibniz Universität Hannover
Field of study
  • Mathematics

Publications

Publications (5)
Preprint
Full-text available
In this work, the dual-weighted residual method is applied to a space-time formulation of nonstationary Stokes and Navier-Stokes flow. Tensor-product space-time finite elements are being used to discretize the variational formulation with discontinuous Galerkin finite elements in time and inf-sup stable Taylor-Hood finite element pairs in space. To...
Article
Full-text available
In this work, we are concerned with neural network guided goal-oriented a posteriori error estimation and adaptivity using the dual weighted residual method. The primal problem is solved using classical Galerkin finite elements. The adjoint problem is solved in strong form with a feedforward neural network using two or three hidden layers. The main...
Article
Full-text available
In this work, we consider the design of a geometric multigrid method with multiplicative Schwarz smoothers for the eddy-current problem and the time-harmonic Maxwell equations. The main purpose is to show numerically that a straightforward application works for the former problem, but not for the latter. The well-known key is a special decompositio...
Preprint
Full-text available
In this work, we are concerned with neural network guided goal-oriented a posteriori error estimation and adaptivity using the dual weighted residual method. The primal problem is solved using classical Galerkin finite elements. The adjoint problem is solved in strong form with a feedforward neural network using two or three hidden layers. The main...

Network

Cited By