| Tensor networks and hierarchical tensors for the solution of high-dimensional partial differential equations M Bachmayr, R Schneider, A Uschmajew Foundations of Computational Mathematics 16, 1423-1472, 2016 | 183 | 2016 |
| Atomic cluster expansion: Completeness, efficiency and stability G Dusson, M Bachmayr, G Csányi, R Drautz, S Etter, C van Der Oord, ... Journal of Computational Physics 454, 110946, 2022 | 144 | 2022 |
| Iterative total variation schemes for nonlinear inverse problems M Bachmayr, M Burger Inverse Problems 25 (10), 105004, 2009 | 125 | 2009 |
| Sparse polynomial approximation of parametric elliptic PDEs. Part II: lognormal coefficients M Bachmayr, A Cohen, R DeVore, G Migliorati ESAIM: Mathematical Modelling and Numerical Analysis 51 (1), 341-363, 2017 | 99 | 2017 |
| Sparse polynomial approximation of parametric elliptic PDEs. Part I: affine coefficients M Bachmayr, A Cohen, G Migliorati ESAIM: Mathematical Modelling and Numerical Analysis 51 (1), 321-339, 2017 | 87 | 2017 |
| Kolmogorov widths and low-rank approximations of parametric elliptic PDEs M Bachmayr, A Cohen Mathematics of Computation 86 (304), 701-724, 2017 | 77 | 2017 |
| Adaptive near-optimal rank tensor approximation for high-dimensional operator equations M Bachmayr, W Dahmen Foundations of Computational Mathematics 15, 839-898, 2015 | 77 | 2015 |
| Parametric PDEs: sparse or low-rank approximations? M Bachmayr, A Cohen, W Dahmen IMA Journal of Numerical Analysis 38 (4), 1661-1708, 2018 | 58 | 2018 |
| Fully discrete approximation of parametric and stochastic elliptic PDEs M Bachmayr, A Cohen, D Dung, C Schwab SIAM Journal on Numerical Analysis 55 (5), 2151-2186, 2017 | 53 | 2017 |
| Representations of Gaussian random fields and approximation of elliptic PDEs with lognormal coefficients M Bachmayr, A Cohen, G Migliorati Journal of Fourier Analysis and Applications 24, 621-649, 2018 | 51 | 2018 |
| Low-rank tensor methods for partial differential equations M Bachmayr Acta Numerica 32, 1-121, 2023 | 47 | 2023 |
| Iterative methods based on soft thresholding of hierarchical tensors M Bachmayr, R Schneider Foundations of Computational Mathematics 17, 1037-1083, 2017 | 40 | 2017 |
| Approximation of high-dimensional rank one tensors M Bachmayr, W Dahmen, R DeVore, L Grasedyck Constructive Approximation 39, 385-395, 2014 | 36 | 2014 |
| Error estimates for Hermite and even-tempered Gaussian approximations in quantum chemistry M Bachmayr, H Chen, R Schneider Numerische Mathematik 128, 137-165, 2014 | 35* | 2014 |
| Stability of low-rank tensor representations and structured multilevel preconditioning for elliptic PDEs M Bachmayr, V Kazeev Foundations of Computational Mathematics 20 (5), 1175-1236, 2020 | 32 | 2020 |
| Sequential sampling for optimal weighted least squares approximations in hierarchical spaces B Arras, M Bachmayr, A Cohen SIAM Journal on Mathematics of Data Science 1 (1), 189-207, 2019 | 31 | 2019 |
| Adaptive low-rank methods: problems on Sobolev spaces M Bachmayr, W Dahmen SIAM Journal on Numerical Analysis 54 (2), 744-796, 2016 | 30 | 2016 |
| Adaptive low-rank wavelet methods and applications to two-electron Schrödinger equations M Bachmayr Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012 | 23 | 2012 |
| Existence of dynamical low-rank approximations to parabolic problems M Bachmayr, H Eisenmann, E Kieri, A Uschmajew Mathematics of Computation 90 (330), 1799-1830, 2021 | 21 | 2021 |
| Iterative total variation methods for nonlinear inverse problems M Bachmayr na, 2007 | 19 | 2007 |
Wolfgang DahmenProfessor for Mathematics, University of South CarolinaVerified email at math.sc.edu
