Adaptive Stochastic Galerkin FEM M Eigel, CJ Gittelson, C Schwab, E Zander Computer Methods in Applied Mechanics and Engineering 270, 247-269, 2014 | 149 | 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 | 76 | 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 | 72 | 2015 |

Adaptive stochastic Galerkin FEM with hierarchical tensor representations M Eigel, M Pfeffer, R Schneider Numerische Mathematik 136, 765-803, 2017 | 69 | 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 | 51 | 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 | 47 | 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 | 46 | 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 | 40 | 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 | 29 | 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 | 28 | 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 | 24 | 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 | 23 | 2019 |

Sampling-free Bayesian inversion with adaptive hierarchical tensor representations M Eigel, M Marschall, R Schneider Inverse Problems 34 (3), 035010, 2018 | 23 | 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 | 20 | 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 | 19 | 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 | 18 | 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 | 17 | 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 | 12 | 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 | 12 | 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 | 11 | 2023 |