Michael Feischl

47 papers Journal 47

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Reduced Basis Method for the Stochastic Landau-Lifshitz-Gilbert Equation. CoRR Andrea Scaglioni, Michael Feischl, Fernando Henríquez
2025	J	`jnl`	BDF2-type integrator for Landau-Lifshitz-Gilbert equation in micromagnetics, part I: unconditional weak convergence to weak solutions. CoRR Michele Aldé, Michael Feischl, Dirk Praetorius
2025	J	`jnl`	Computational Math with Neural Networks is Hard. CoRR Michael Feischl, Fabian Zehetgruber
2025	J	`jnl`	On full linear convergence and optimal complexity of adaptive FEM with inexact solver. Comput. Math. Appl. Philipp Bringmann, Michael Feischl, Ani Miraçi, Dirk Praetorius, Julian Streitberger
2025	J	`jnl`	Optimal Time-Adaptivity for Parabolic Problems with applications to Model Order Reduction. CoRR Michael Feischl, Fernando Henríquez, David Niederkofler
2025	J	`jnl`	Optimal adaptive implicit time stepping. CoRR Michael Feischl, David Niederkofler
2025	J	`jnl`	Sparse Grid Approximation of Nonlinear SPDEs: The Landau-Lifshitz-Gilbert Equation. SIAM/ASA J. Uncertain. Quantification Xin An, Josef Dick, Michael Feischl, Andrea Scaglioni, Thanh Tran
2025	J	`jnl`	Well-Posedness of Discretizations for Fractional Elasto-Plasticity. CoRR Michael Feischl, David Niederkofler, Barbara I. Wohlmuth
2024	J	`jnl`	Adaptive Image Compression via Optimal Mesh Refinement. Comput. Methods Appl. Math. Michael Feischl, Hubert Hackl
2024	J	`jnl`	Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 1). Comput. Methods Appl. Math. Michael Feischl, Dirk Praetorius, Michele Ruggeri
2024	J	`jnl`	Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 2). Comput. Methods Appl. Math. Michael Feischl, Dirk Praetorius, Michele Ruggeri
2024	J	`jnl`	Regularized dynamical parametric approximation. CoRR Michael Feischl, Caroline Lasser, Christian Lubich, Jörg Nick
2024	J	`jnl`	Towards optimal hierarchical training of neural networks. CoRR Michael Feischl, Alexander Rieder, Fabian Zehetgruber
2023	J	`jnl`	Adaptive Image Compression via Optimal Mesh Refinement. CoRR Michael Feischl, Hubert Hackl
2023	J	`jnl`	Adaptive mesh refinement for the Landau-Lifshitz-Gilbert equation. CoRR Jan Bohn, Willy Dörfler, Michael Feischl, Stefan Karch
2023	J	`jnl`	FEM-BEM Coupling for the Maxwell-Landau-Lifshitz-Gilbert Equations via Convolution Quadrature: Weak Form and Numerical Approximation. Comput. Methods Appl. Math. Jan Bohn, Michael Feischl, Balázs Kovács
2023	J	`jnl`	On full linear convergence and optimal complexity of adaptive FEM with inexact solver. CoRR Philipp Bringmann, Michael Feischl, Ani Miraçi, Dirk Praetorius, Julian Streitberger
2023	J	`jnl`	Sparse grid approximation of the stochastic Landau-Lifshitz-Gilbert equation. CoRR Xin An, Josef Dick, Michael Feischl, Andrea Scaglioni, Thanh Tran
2022	J	`jnl`	Inf-sup stability implies quasi-orthogonality. Math. Comput. Michael Feischl
2021	J	`jnl`	A quasi-Monte Carlo data compression algorithm for machine learning. J. Complex. Josef Dick, Michael Feischl
2021	J	`jnl`	Convergence of adaptive stochastic collocation with finite elements. Comput. Math. Appl. Michael Feischl, Andrea Scaglioni
2021	J	`jnl`	Higher-order linearly implicit full discretization of the Landau-Lifshitz-Gilbert equation. Math. Comput. Georgios Akrivis, Michael Feischl, Balázs Kovács, Christian Lubich
2021	J	`jnl`	Recurrent neural networks as optimal mesh refinement strategies. Comput. Math. Appl. Jan Bohn, Michael Feischl
2020	J	`jnl`	A quasi-Monte Carlo data compression algorithm for machine learning. CoRR Josef Dick, Michael Feischl
2020	J	`jnl`	Convergence of adaptive stochastic collocation with finite elements. CoRR Michael Feischl, Andrea Scaglioni
2020	J	`jnl`	Exponential convergence in \(H^1\) of Numerische Mathematik Michael Feischl, Christoph Schwab
2020	J	`jnl`	Inf-sup stability implies quasi-orthogonality. CoRR Michael Feischl
2020	J	`jnl`	Sparse Compression of Expected Solution Operators. SIAM J. Numer. Anal. Michael Feischl, Daniel Peterseim
2019	J	`jnl`	Correction to: Fast random field generation with Numerische Mathematik Michael Feischl, Frances Y. Kuo, Ian H. Sloan
2019	J	`jnl`	Improved Efficiency of a Multi-Index FEM for Computational Uncertainty Quantification. SIAM J. Numer. Anal. Josef Dick, Michael Feischl, Christoph Schwab
2019	J	`jnl`	Optimality of a Standard Adaptive Finite Element Method for the Stokes Problem. SIAM J. Numer. Anal. Michael Feischl
2019	J	`jnl`	Recurrent Neural Networks as Optimal Mesh Refinement Strategies. CoRR Jan Bohn, Michael Feischl
2018	J	`jnl`	Fast random field generation with Numerische Mathematik Michael Feischl, Frances Y. Kuo, Ian H. Sloan
2017	J	`jnl`	Existence of Regular Solutions of the Landau-Lifshitz-Gilbert Equation in 3D with Natural Boundary Conditions. SIAM J. Math. Anal. Michael Feischl, Thanh Tran
2017	J	`jnl`	Local inverse estimates for non-local boundary integral operators. Math. Comput. Markus Aurada, Michael Feischl, Thomas Führer, Michael Karkulik, Jens Markus Melenk, Dirk Praetorius
2017	J	`jnl`	Optimal convergence for adaptive IGA boundary element methods for weakly-singular integral equations. Numerische Mathematik Michael Feischl, Gregor Gantner, Alexander Haberl, Dirk Praetorius
2017	J	`jnl`	The Eddy Current-LLG Equations: FEM-BEM Coupling and A Priori Error Estimates. SIAM J. Numer. Anal. Michael Feischl, Thanh Tran
2016	J	`jnl`	Adaptive boundary element methods for optimal convergence of point errors. Numerische Mathematik Michael Feischl, Gregor Gantner, Alexander Haberl, Dirk Praetorius, Thomas Führer
2016	J	`jnl`	An Abstract Analysis of Optimal Goal-Oriented Adaptivity. SIAM J. Numer. Anal. Michael Feischl, Dirk Praetorius, Kristoffer G. van der Zee
2015	J	`jnl`	Stability of symmetric and nonsymmetric FEM-BEM couplings for nonlinear elasticity problems. Numerische Mathematik Michael Feischl, Thomas Führer, Michael Karkulik, Dirk Praetorius
2014	J	`jnl`	Adaptive FEM with Optimal Convergence Rates for a Certain Class of Nonsymmetric and Possibly Nonlinear Problems. SIAM J. Numer. Anal. Michael Feischl, Thomas Führer, Dirk Praetorius
2014	J	`jnl`	Axioms of adaptivity. Comput. Math. Appl. Carsten Carstensen, Michael Feischl, Marcus Page, Dirk Praetorius
2014	J	`jnl`	Convergence and quasi-optimality of adaptive FEM with inhomogeneous Dirichlet data. J. Comput. Appl. Math. Michael Feischl, Marcus Page, Dirk Praetorius
2014	J	`jnl`	Convergence of Adaptive BEM and Adaptive FEM-BEM Coupling for Estimators Without h-Weighting Factor. Comput. Methods Appl. Math. Michael Feischl, Thomas Führer, Gregor Mitscha-Eibl, Dirk Praetorius, Ernst P. Stephan
2014	J	`jnl`	HILBERT - a MATLAB implementation of adaptive 2D-BEM - HILBERT Is a Lovely Boundary Element Research Tool. Numer. Algorithms Markus Aurada, Michael Ebner, Michael Feischl, Samuel Ferraz-Leite, Thomas Führer, Petra Goldenits, Michael Karkulik, Markus Mayr, Dirk Praetorius
2013	J	`jnl`	Efficiency and Optimality of Some Weighted-Residual Error Estimator for Adaptive 2D Boundary Element Methods. Comput. Methods Appl. Math. Markus Aurada, Michael Feischl, Thomas Führer, Michael Karkulik, Dirk Praetorius
2013	J	`jnl`	Quasi-optimal Convergence Rate for an Adaptive Boundary Element Method. SIAM J. Numer. Anal. Michael Feischl, Michael Karkulik, Jens Markus Melenk, Dirk Praetorius