Martin Eigel
Martin Eigel
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Cited by
Adaptive Stochastic Galerkin FEM
M Eigel, CJ Gittelson, C Schwab, E Zander
Computer Methods in Applied Mechanics and Engineering 270, 247-269, 2014
A review of unified a posteriori finite element error control
C Carstensen, M Eigel, RHW Hoppe, C Löbhard
Numerical Mathematics: Theory, Methods and Applications 5 (4), 509-558, 2012
A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes
M Eigel, CJ Gittelson, C Schwab, E Zander
ESAIM: Mathematical Modelling and Numerical Analysis-Modélisation …, 2015
Adaptive stochastic Galerkin FEM with hierarchical tensor representations
M Eigel, M Pfeffer, R Schneider
Numerische Mathematik 136, 765-803, 2017
Influence of cell shape, inhomogeneities and diffusion barriers in cell polarization models
W Giese, M Eigel, S Westerheide, C Engwer, E Klipp
Physical biology 12 (6), 066014, 2015
An adaptive multilevel Monte Carlo method with stochastic bounds for quantities of interest with uncertain data
M Eigel, C Merdon, J Neumann
SIAM/ASA Journal on Uncertainty Quantification 4 (1), 1219-1245, 2016
Variational Monte Carlo—bridging concepts of machine learning and high-dimensional partial differential equations
M Eigel, R Schneider, P Trunschke, S Wolf
Advances in Computational Mathematics 45, 2503-2532, 2019
Computational competition of symmetric mixed FEM in linear elasticity
C Carstensen, M Eigel, J Gedicke
Computer methods in applied mechanics and engineering 200 (41-44), 2903-2915, 2011
Local equilibration error estimators for guaranteed error control in adaptive stochastic higher-order Galerkin finite element methods
M Eigel, C Merdon
SIAM/ASA Journal on Uncertainty Quantification 4 (1), 1372-1397, 2016
Adaptive stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations
M Eigel, M Marschall, M Pfeffer, R Schneider
Numerische Mathematik 145, 655-692, 2020
Assessment and design of an engineering structure with polymorphic uncertainty quantification
I Papaioannou, M Daub, M Drieschner, F Duddeck, M Ehre, L Eichner, ...
GAMM‐Mitteilungen 42 (2), e201900009, 2019
Non-intrusive tensor reconstruction for high-dimensional random PDEs
M Eigel, J Neumann, R Schneider, S Wolf
Computational Methods in Applied Mathematics 19 (1), 39-53, 2019
Sampling-free Bayesian inversion with adaptive hierarchical tensor representations
M Eigel, M Marschall, R Schneider
Inverse Problems 34 (3), 035010, 2018
On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion
M Eigel, OG Ernst, B Sprungk, L Tamellini
SIAM Journal on Numerical Analysis 60 (2), 659-687, 2022
Alea-a python framework for spectral methods and low-rank approximations in uncertainty quantification
M Eigel, E Zander
Low rank surrogates for polymorphic fields 33, 2020
Reproducing kernel Hilbert spaces and variable metric algorithms in PDE-constrained shape optimization
M Eigel, K Sturm
Optimization Methods and Software 33 (2), 268-296, 2018
Convergence bounds for empirical nonlinear least-squares
M Eigel, R Schneider, P Trunschke
ESAIM: Mathematical Modelling and Numerical Analysis 56 (1), 79-104, 2022
Low-rank tensor reconstruction of concentrated densities with application to Bayesian inversion
M Eigel, R Gruhlke, M Marschall
Statistics and Computing 32 (2), 27, 2022
SDE based regression for linear random PDEs
F Anker, C Bayer, M Eigel, M Ladkau, J Neumann, J Schoenmakers
SIAM Journal on Scientific Computing 39 (3), A1168-A1200, 2017
Pricing high-dimensional Bermudan options with hierarchical tensor formats
C Bayer, M Eigel, L Sallandt, P Trunschke
SIAM Journal on Financial Mathematics 14 (2), 383-406, 2023
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