Michael Feischl

According to our database, Michael Feischl authored at least 34 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

On full linear convergence and optimal complexity of adaptive FEM with inexact solver.

Philipp Bringmann, Michael Feischl, Ani Miraçi, Dirk Praetorius, Julian Streitberger

Comput. Math. Appl., 2025

2024

Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 2).

Michael Feischl, Dirk Praetorius, Michele Ruggeri

Comput. Methods Appl. Math., July, 2024

Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 1).

Michael Feischl, Dirk Praetorius, Michele Ruggeri

Comput. Methods Appl. Math., April, 2024

Adaptive Image Compression via Optimal Mesh Refinement.

Michael Feischl, Hubert Hackl

Comput. Methods Appl. Math., April, 2024

Towards optimal hierarchical training of neural networks.

Michael Feischl, Alexander Rieder, Fabian Zehetgruber

CoRR, 2024

Regularized dynamical parametric approximation.

Michael Feischl, Caroline Lasser, Christian Lubich, Jörg Nick

CoRR, 2024

2023

Sparse grid approximation of the stochastic Landau-Lifshitz-Gilbert equation.

Xin An, Josef Dick, Michael Feischl, Andrea Scaglioni, Thanh Tran

CoRR, 2023

Adaptive mesh refinement for the Landau-Lifshitz-Gilbert equation.

Jan Bohn, Willy Dörfler, Michael Feischl, Stefan Karch

CoRR, 2023

FEM-BEM Coupling for the Maxwell-Landau-Lifshitz-Gilbert Equations via Convolution Quadrature: Weak Form and Numerical Approximation.

Jan Bohn, Michael Feischl, Balázs Kovács

Comput. Methods Appl. Math., 2023

2022

Inf-sup stability implies quasi-orthogonality.

Michael Feischl

Math. Comput., 2022

2021

Higher-order linearly implicit full discretization of the Landau-Lifshitz-Gilbert equation.

Georgios Akrivis, Michael Feischl, Balázs Kovács, Christian Lubich

Math. Comput., 2021

A quasi-Monte Carlo data compression algorithm for machine learning.

Josef Dick, Michael Feischl

J. Complex., 2021

Convergence of adaptive stochastic collocation with finite elements.

Michael Feischl, Andrea Scaglioni

Comput. Math. Appl., 2021

Recurrent neural networks as optimal mesh refinement strategies.

Jan Bohn, Michael Feischl

Comput. Math. Appl., 2021

2020

Sparse Compression of Expected Solution Operators.

Michael Feischl, Daniel Peterseim

SIAM J. Numer. Anal., 2020

Exponential convergence in \(H^1\) of <i>hp</i>-FEM for Gevrey regularity with isotropic singularities.

Michael Feischl, Christoph Schwab

Numerische Mathematik, 2020

2019

Optimality of a Standard Adaptive Finite Element Method for the Stokes Problem.

Michael Feischl

SIAM J. Numer. Anal., 2019

Improved Efficiency of a Multi-Index FEM for Computational Uncertainty Quantification.

Josef Dick, Michael Feischl, Christoph Schwab

SIAM J. Numer. Anal., 2019

Correction to: Fast random field generation with <i>H</i>-matrices.

Michael Feischl, Frances Y. Kuo, Ian H. Sloan

Numerische Mathematik, 2019

2018

Fast random field generation with <i>H</i>-matrices.

Michael Feischl, Frances Y. Kuo, Ian H. Sloan

Numerische Mathematik, 2018

2017

The Eddy Current-LLG Equations: FEM-BEM Coupling and A Priori Error Estimates.

Michael Feischl, Thanh Tran

SIAM J. Numer. Anal., 2017

Existence of Regular Solutions of the Landau-Lifshitz-Gilbert Equation in 3D with Natural Boundary Conditions.

Michael Feischl, Thanh Tran

SIAM J. Math. Anal., 2017

Optimal convergence for adaptive IGA boundary element methods for weakly-singular integral equations.

Michael Feischl, Gregor Gantner, Alexander Haberl, Dirk Praetorius

Numerische Mathematik, 2017

Local inverse estimates for non-local boundary integral operators.

Markus Aurada, Michael Feischl, Thomas Führer, Michael Karkulik, Jens Markus Melenk, Dirk Praetorius

Math. Comput., 2017

2016

An Abstract Analysis of Optimal Goal-Oriented Adaptivity.

Michael Feischl, Dirk Praetorius, Kristoffer G. van der Zee

SIAM J. Numer. Anal., 2016

Adaptive boundary element methods for optimal convergence of point errors.

Michael Feischl, Gregor Gantner, Alexander Haberl, Dirk Praetorius, Thomas Führer

Numerische Mathematik, 2016

2015

Stability of symmetric and nonsymmetric FEM-BEM couplings for nonlinear elasticity problems.

Michael Feischl, Thomas Führer, Michael Karkulik, Dirk Praetorius

Numerische Mathematik, 2015

2014

Adaptive FEM with Optimal Convergence Rates for a Certain Class of Nonsymmetric and Possibly Nonlinear Problems.

Michael Feischl, Thomas Führer, Dirk Praetorius

SIAM J. Numer. Anal., 2014

HILBERT - a MATLAB implementation of adaptive 2D-BEM - HILBERT Is a Lovely Boundary Element Research Tool.

Markus Aurada, Michael Ebner, Michael Feischl, Samuel Ferraz-Leite, Thomas Führer, Petra Goldenits, Michael Karkulik, Markus Mayr, Dirk Praetorius

Numer. Algorithms, 2014

Convergence and quasi-optimality of adaptive FEM with inhomogeneous Dirichlet data.

Michael Feischl, Marcus Page, Dirk Praetorius

J. Comput. Appl. Math., 2014

Convergence of Adaptive BEM and Adaptive FEM-BEM Coupling for Estimators Without h-Weighting Factor.

Michael Feischl, Thomas Führer, Gregor Mitscha-Eibl, Dirk Praetorius, Ernst P. Stephan

Comput. Methods Appl. Math., 2014

Axioms of adaptivity.

Carsten Carstensen, Michael Feischl, Marcus Page, Dirk Praetorius

Comput. Math. Appl., 2014

2013

Quasi-optimal Convergence Rate for an Adaptive Boundary Element Method.

Michael Feischl, Michael Karkulik, Jens Markus Melenk, Dirk Praetorius

SIAM J. Numer. Anal., 2013

Efficiency and Optimality of Some Weighted-Residual Error Estimator for Adaptive 2D Boundary Element Methods.

Markus Aurada, Michael Feischl, Thomas Führer, Michael Karkulik, Dirk Praetorius

Comput. Methods Appl. Math., 2013