| Optimal approximation with sparsely connected deep neural networks H Bolcskei, P Grohs, G Kutyniok, P Petersen SIAM Journal on Mathematics of Data Science 1 (1), 8-45, 2019 | 316 | 2019 |
| A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations P Grohs, F Hornung, A Jentzen, P Von Wurstemberger Memoirs of the American Mathematical Society 284 (1410), 2023 | 280 | 2023 |
| Deep neural network approximation theory D Elbrächter, D Perekrestenko, P Grohs, H Bölcskei IEEE Transactions on Information Theory, 2019 | 279 | 2019 |
| The modern mathematics of deep learning J Berner, P Grohs, G Kutyniok, P Petersen arXiv preprint arXiv:2105.04026 78, 2021 | 274* | 2021 |
| Solving the Kolmogorov PDE by means of deep learning C Beck, S Becker, P Grohs, N Jaafari, A Jentzen Journal of Scientific Computing 88, 1-28, 2021 | 230 | 2021 |
| Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of … J Berner, P Grohs, A Jentzen SIAM Journal on Mathematics of Data Science 2 (3), 631-657, 2020 | 218 | 2020 |
| DNN expression rate analysis of high-dimensional PDEs: application to option pricing D Elbrächter, P Grohs, A Jentzen, C Schwab Constructive Approximation 55 (1), 3-71, 2022 | 143 | 2022 |
| Phase retrieval: uniqueness and stability P Grohs, S Koppensteiner, M Rathmair SIAM Review 62 (2), 301-350, 2020 | 89 | 2020 |
| Laguerre minimal surfaces, isotropic geometry and linear elasticity H Pottmann, P Grohs, NJ Mitra Advances in computational mathematics 31 (4), 391, 2009 | 89 | 2009 |
| Parabolic molecules P Grohs, G Kutyniok Foundations of Computational Mathematics 14, 299-337, 2014 | 86 | 2014 |
| Stable phase retrieval in infinite dimensions R Alaifari, I Daubechies, P Grohs, R Yin Foundations of Computational Mathematics 19, 869-900, 2019 | 80 | 2019 |
| ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds P Grohs, S Hosseini Advances in Computational Mathematics 42, 333-360, 2016 | 80 | 2016 |
| Group testing for SARS-CoV-2 allows for up to 10-fold efficiency increase across realistic scenarios and testing strategies CM Verdun, T Fuchs, P Harar, D Elbrächter, DS Fischer, J Berner, ... Frontiers in Public Health 9, 583377, 2021 | 72 | 2021 |
| Phase retrieval in the general setting of continuous frames for Banach spaces R Alaifari, P Grohs SIAM journal on mathematical analysis 49 (3), 1895-1911, 2017 | 72 | 2017 |
| Continuous shearlet frames and resolution of the wavefront set P Grohs Monatshefte für Mathematik 164 (4), 393-426, 2011 | 72 | 2011 |
| Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions P Grohs, L Herrmann IMA Journal of Numerical Analysis 42 (3), 2055-2082, 2022 | 65 | 2022 |
| Stable Gabor phase retrieval and spectral clustering P Grohs, M Rathmair Communications on Pure and Applied Mathematics 72 (5), 981-1043, 2019 | 62 | 2019 |
| Space-time error estimates for deep neural network approximations for differential equations P Grohs, F Hornung, A Jentzen, P Zimmermann Advances in Computational Mathematics 49 (1), 4, 2023 | 60 | 2023 |
| Numerically solving parametric families of high-dimensional Kolmogorov partial differential equations via deep learning J Berner, M Dablander, P Grohs Advances in Neural Information Processing Systems 33, 16615-16627, 2020 | 60 | 2020 |
| Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks M Scherbela, R Reisenhofer, L Gerard, P Marquetand, P Grohs Nature Computational Science 2 (5), 331-341, 2022 | 59 | 2022 |
