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)

Quasi-optimal Convergence Rate for an Adaptive Boundary Element Method. - Michael Feischl; Michael Karkulik; Jens Markus Melenk; Dirk Praetorius (SIAM J. Numer. Anal., 2013)

Axioms of adaptivity. - Carsten Carstensen; Michael Feischl; Marcus Page; Dirk Praetorius (Comput. Math. Appl., 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)

Convergence and quasi-optimality of adaptive FEM with inhomogeneous Dirichlet data. - Michael Feischl; Marcus Page; Dirk Praetorius (J. Comput. Appl. Math., 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)

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)

Stability of symmetric and nonsymmetric FEM-BEM couplings for nonlinear elasticity problems. - Michael Feischl; Thomas Führer; Michael Karkulik; Dirk Praetorius (Numerische Mathematik, 2015)

Adaptive boundary element methods for optimal convergence of point errors. - Michael Feischl; Gregor Gantner; Alexander Haberl; Dirk Praetorius; Thomas Führer (Numerische Mathematik, 2016)

An Abstract Analysis of Optimal Goal-Oriented Adaptivity. - Michael Feischl; Dirk Praetorius; Kristoffer G. van der Zee (SIAM J. Numer. Anal., 2016)

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)

Optimal convergence for adaptive IGA boundary element methods for weakly-singular integral equations. - Michael Feischl; Gregor Gantner; Alexander Haberl; Dirk Praetorius (Numerische Mathematik, 2017)

Existence of Regular Solutions of the Landau-Lifshitz-Gilbert Equation in 3D with Natural Boundary Conditions. - Michael Feischl; Thanh Tran 003 (SIAM J. Math. Anal., 2017)

The Eddy Current-LLG Equations: FEM-BEM Coupling and A Priori Error Estimates. - Michael Feischl; Thanh Tran 003 (SIAM J. Numer. Anal., 2017)

Fast random field generation with H-matrices. - Michael Feischl; Frances Y. Kuo; Ian H. Sloan (Numerische Mathematik, 2018)

Correction to: Fast random field generation with H-matrices. - Michael Feischl; Frances Y. Kuo; Ian H. Sloan (Numerische Mathematik, 2019)

Improved Efficiency of a Multi-Index FEM for Computational Uncertainty Quantification. - Josef Dick; Michael Feischl; Christoph Schwab (SIAM J. Numer. Anal., 2019)

Optimality of a Standard Adaptive Finite Element Method for the Stokes Problem. - Michael Feischl (SIAM J. Numer. Anal., 2019)

Exponential convergence in (H^1 ) of hp-FEM for Gevrey regularity with isotropic singularities. - Michael Feischl; Christoph Schwab (Numerische Mathematik, 2020)

Sparse Compression of Expected Solution Operators. - Michael Feischl; Daniel Peterseim (SIAM J. Numer. Anal., 2020)

Recurrent neural networks as optimal mesh refinement strategies. - Jan Bohn; Michael Feischl (Comput. Math. Appl., 2021)

Convergence of adaptive stochastic collocation with finite elements. - Michael Feischl; Andrea Scaglioni (Comput. Math. Appl., 2021)

A quasi-Monte Carlo data compression algorithm for machine learning. - Josef Dick; Michael Feischl (J. Complex., 2021)

Higher-order linearly implicit full discretization of the Landau-Lifshitz-Gilbert equation. - Georgios Akrivis; Michael Feischl; Balázs Kovács; Christian Lubich (Math. Comput., 2021)

Inf-sup stability implies quasi-orthogonality. - Michael Feischl (Math. Comput., 2022)

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)

Adaptive mesh refinement for the Landau-Lifshitz-Gilbert equation. - Jan Bohn; Willy Dörfler; Michael Feischl; Stefan Karch (CoRR, 2023)

Sparse grid approximation of the stochastic Landau-Lifshitz-Gilbert equation. - Xin An; Josef Dick; Michael Feischl; Andrea Scaglioni; Thanh Tran 003 (CoRR, 2023)

Regularized dynamical parametric approximation. - Michael Feischl; Caroline Lasser; Christian Lubich; Jörg Nick (CoRR, 2024)

Towards optimal hierarchical training of neural networks. - Michael Feischl; Alexander Rieder; Fabian Zehetgruber (CoRR, 2024)

Adaptive Image Compression via Optimal Mesh Refinement. - Michael Feischl; Hubert Hackl (Comput. Methods Appl. Math., 2024)

Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 1). - Michael Feischl; Dirk Praetorius; Michele Ruggeri (Comput. Methods Appl. Math., 2024)

Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 2). - Michael Feischl; Dirk Praetorius; Michele Ruggeri (Comput. Methods Appl. Math., 2024)

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)

Sparse Grid Approximation of Nonlinear SPDEs: The Landau-Lifshitz-Gilbert Equation. - Xin An; Josef Dick; Michael Feischl; Andrea Scaglioni; Thanh Tran 003 (SIAM/ASA J. Uncertain. Quantification, 2025)

Well-Posedness of Discretizations for Fractional Elasto-Plasticity. - Michael Feischl; David Niederkofler; Barbara I. Wohlmuth (CoRR, 2025)

Computational Math with Neural Networks is Hard. - Michael Feischl; Fabian Zehetgruber (CoRR, 2025)

Optimal adaptive implicit time stepping. - Michael Feischl; David Niederkofler (CoRR, 2025)