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Michael Feischl
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2020 – today
- 2024
- [j29]Michael Feischl, Dirk Praetorius
, Michele Ruggeri
:
Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 1). Comput. Methods Appl. Math. 24(2): 279-282 (2024) - [j28]Michael Feischl
, Hubert Hackl:
Adaptive Image Compression via Optimal Mesh Refinement. Comput. Methods Appl. Math. 24(2): 325-344 (2024) - [j27]Michael Feischl
, Dirk Praetorius
, Michele Ruggeri
:
Computational Methods in Applied Mathematics (CMAM 2022 Conference, Part 2). Comput. Methods Appl. Math. 24(3): 529-532 (2024) - [i10]Michael Feischl, Caroline Lasser, Christian Lubich, Jörg Nick:
Regularized dynamical parametric approximation. CoRR abs/2403.19234 (2024) - [i9]Michael Feischl, Alexander Rieder, Fabian Zehetgruber:
Towards optimal hierarchical training of neural networks. CoRR abs/2407.02242 (2024) - 2023
- [j26]Jan Bohn, Michael Feischl
, Balázs Kovács:
FEM-BEM Coupling for the Maxwell-Landau-Lifshitz-Gilbert Equations via Convolution Quadrature: Weak Form and Numerical Approximation. Comput. Methods Appl. Math. 23(1): 19-48 (2023) - [i8]Jan Bohn, Willy Dörfler, Michael Feischl, Stefan Karch:
Adaptive mesh refinement for the Landau-Lifshitz-Gilbert equation. CoRR abs/2303.07463 (2023) - [i7]Michael Feischl, Hubert Hackl:
Adaptive Image Compression via Optimal Mesh Refinement. CoRR abs/2304.01640 (2023) - [i6]Xin An, Josef Dick, Michael Feischl, Andrea Scaglioni, Thanh Tran:
Sparse grid approximation of the stochastic Landau-Lifshitz-Gilbert equation. CoRR abs/2310.11225 (2023) - [i5]Philipp Bringmann, Michael Feischl, Ani Miraçi, Dirk Praetorius, Julian Streitberger:
On full linear convergence and optimal complexity of adaptive FEM with inexact solver. CoRR abs/2311.15738 (2023) - 2022
- [j25]Michael Feischl:
Inf-sup stability implies quasi-orthogonality. Math. Comput. 91(337): 2059-2094 (2022) - 2021
- [j24]Jan Bohn, Michael Feischl
:
Recurrent neural networks as optimal mesh refinement strategies. Comput. Math. Appl. 97: 61-76 (2021) - [j23]Michael Feischl
, Andrea Scaglioni
:
Convergence of adaptive stochastic collocation with finite elements. Comput. Math. Appl. 98: 139-156 (2021) - [j22]Josef Dick, Michael Feischl
:
A quasi-Monte Carlo data compression algorithm for machine learning. J. Complex. 67: 101587 (2021) - [j21]Georgios Akrivis, Michael Feischl
, Balázs Kovács, Christian Lubich:
Higher-order linearly implicit full discretization of the Landau-Lifshitz-Gilbert equation. Math. Comput. 90(329): 995-1038 (2021) - 2020
- [j20]Michael Feischl
, Christoph Schwab:
Exponential convergence in \(H^1\) of hp-FEM for Gevrey regularity with isotropic singularities. Numerische Mathematik 144(2): 323-346 (2020) - [j19]Michael Feischl
, Daniel Peterseim
:
Sparse Compression of Expected Solution Operators. SIAM J. Numer. Anal. 58(6): 3144-3164 (2020) - [i4]Josef Dick, Michael Feischl:
A quasi-Monte Carlo data compression algorithm for machine learning. CoRR abs/2004.02491 (2020) - [i3]Michael Feischl:
Inf-sup stability implies quasi-orthogonality. CoRR abs/2008.12198 (2020) - [i2]Michael Feischl, Andrea Scaglioni:
Convergence of adaptive stochastic collocation with finite elements. CoRR abs/2008.12591 (2020)
2010 – 2019
- 2019
- [j18]Michael Feischl
, Frances Y. Kuo, Ian H. Sloan
:
Correction to: Fast random field generation with H-matrices. Numerische Mathematik 142(3): 787 (2019) - [j17]Michael Feischl
:
Optimality of a Standard Adaptive Finite Element Method for the Stokes Problem. SIAM J. Numer. Anal. 57(3): 1124-1157 (2019) - [j16]Josef Dick
, Michael Feischl
, Christoph Schwab:
Improved Efficiency of a Multi-Index FEM for Computational Uncertainty Quantification. SIAM J. Numer. Anal. 57(4): 1744-1769 (2019) - [i1]Jan Bohn, Michael Feischl:
Recurrent Neural Networks as Optimal Mesh Refinement Strategies. CoRR abs/1909.04275 (2019) - 2018
- [j15]Michael Feischl
, Frances Y. Kuo, Ian H. Sloan
:
Fast random field generation with H-matrices. Numerische Mathematik 140(3): 639-676 (2018) - 2017
- [j14]Markus Aurada, Michael Feischl
, Thomas Führer
, Michael Karkulik, Jens Markus Melenk, Dirk Praetorius
:
Local inverse estimates for non-local boundary integral operators. Math. Comput. 86(308): 2651-2686 (2017) - [j13]Michael Feischl
, Gregor Gantner
, Alexander Haberl, Dirk Praetorius
:
Optimal convergence for adaptive IGA boundary element methods for weakly-singular integral equations. Numerische Mathematik 136(1): 147-182 (2017) - [j12]Michael Feischl
, Thanh Tran:
Existence of Regular Solutions of the Landau-Lifshitz-Gilbert Equation in 3D with Natural Boundary Conditions. SIAM J. Math. Anal. 49(6): 4470-4490 (2017) - [j11]Michael Feischl
, Thanh Tran:
The Eddy Current-LLG Equations: FEM-BEM Coupling and A Priori Error Estimates. SIAM J. Numer. Anal. 55(4): 1786-1819 (2017) - 2016
- [j10]Michael Feischl
, Gregor Gantner
, Alexander Haberl, Dirk Praetorius
, Thomas Führer
:
Adaptive boundary element methods for optimal convergence of point errors. Numerische Mathematik 132(3): 541-567 (2016) - [j9]Michael Feischl
, Dirk Praetorius
, Kristoffer G. van der Zee:
An Abstract Analysis of Optimal Goal-Oriented Adaptivity. SIAM J. Numer. Anal. 54(3): 1423-1448 (2016) - 2015
- [j8]Michael Feischl
, Thomas Führer
, Michael Karkulik, Dirk Praetorius
:
Stability of symmetric and nonsymmetric FEM-BEM couplings for nonlinear elasticity problems. Numerische Mathematik 130(2): 199-223 (2015) - 2014
- [j7]Carsten Carstensen, Michael Feischl
, Marcus Page, Dirk Praetorius
:
Axioms of adaptivity. Comput. Math. Appl. 67(6): 1195-1253 (2014) - [j6]Michael Feischl
, Thomas Führer
, Gregor Mitscha-Eibl
, Dirk Praetorius
, Ernst P. Stephan
:
Convergence of Adaptive BEM and Adaptive FEM-BEM Coupling for Estimators Without h-Weighting Factor. Comput. Methods Appl. Math. 14(4): 485-508 (2014) - [j5]Michael Feischl
, Marcus Page, Dirk Praetorius
:
Convergence and quasi-optimality of adaptive FEM with inhomogeneous Dirichlet data. J. Comput. Appl. Math. 255: 481-501 (2014) - [j4]Markus Aurada, Michael Ebner
, Michael Feischl
, Samuel Ferraz-Leite, Thomas Führer
, Petra Goldenits, Michael Karkulik, Markus Mayr, Dirk Praetorius
:
HILBERT - a MATLAB implementation of adaptive 2D-BEM - HILBERT Is a Lovely Boundary Element Research Tool. Numer. Algorithms 67(1): 1-32 (2014) - [j3]Michael Feischl
, Thomas Führer
, Dirk Praetorius
:
Adaptive FEM with Optimal Convergence Rates for a Certain Class of Nonsymmetric and Possibly Nonlinear Problems. SIAM J. Numer. Anal. 52(2): 601-625 (2014) - 2013
- [j2]Markus Aurada, Michael Feischl
, Thomas Führer
, Michael Karkulik, Dirk Praetorius
:
Efficiency and Optimality of Some Weighted-Residual Error Estimator for Adaptive 2D Boundary Element Methods. Comput. Methods Appl. Math. 13(3): 305-332 (2013) - [j1]Michael Feischl
, Michael Karkulik, Jens Markus Melenk, Dirk Praetorius
:
Quasi-optimal Convergence Rate for an Adaptive Boundary Element Method. SIAM J. Numer. Anal. 51(2): 1327-1348 (2013)
Coauthor Index
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last updated on 2024-08-23 19:24 CEST by the dblp team
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