activity
20182025
most citedParameter learning and fractional differential operators: application in image regularization and decomposition

5 citations · 12 across the 15 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

math.NA20202 cited

Orthogonality relations of Crouzeix-Raviart and Raviart-Thomas finite element spaces

Sören Bartels, Zhangxian Wang

Identities that relate projections of Raviart-Thomas finite element vector fields to discrete gradients of Crouzeix-Raviart finite element functions are derived under general condi…

math.NA2020

Simulation of constrained elastic curves and application to a conical sheet indentation problem

Sören Bartels

We consider variational problems that model the bending behavior of curves that are constrained to belong to given hypersurfaces. Finite element discretizations of corresponding fu…

math.NA2020

Error estimates for a class of discontinuous Galerkin methods for nonsmooth problems via convex duality relations

Sören Bartels

We devise and analyze a class of interior penalty discontinuous Galerkin methods for nonlinear and nonsmooth variational problems. Discrete duality relations are derived that lead…

math.NA2020

Stable Gradient Flow Discretizations for Simulating Bilayer Plate Bending with Isometry and Obstacle Constraints

Sören Bartels, Christian Palus

Bilayer plates are compound materials that exhibit large bending deformations when exposed to environmental changes that lead to different mechanical responses in the involved mate…

math.NA2020

Nonconforming discretizations of convex minimization problems and precise relations to mixed methods

Sören Bartels

This article discusses nonconforming finite element methods for convex minimization problems and systematically derives dual mixed formulations. Duality relations lead to simple er…

math.OC20205 cited

Parameter learning and fractional differential operators: application in image regularization and decomposition

Sören Bartels, Nico Weber

In this paper, we focus on learning optimal parameters for PDE-based image regularization and decomposition. First we learn the regularization parameter and the differential operat…