| 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 | 366 | 2019 |
| Deep neural network approximation theory D Elbrächter, D Perekrestenko, P Grohs, H Bölcskei IEEE Transactions on Information Theory, 2019 | 360 | 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 | 327 | 2023 |
| Solving the Kolmogorov PDE by means of deep learning C Beck, S Becker, P Grohs, N Jaafari, A Jentzen Journal of Scientific Computing 88 (3), 73, 2021 | 272 | 2021 |
| The modern mathematics of deep learning J Berner, P Grohs, G Kutyniok, P Petersen arXiv preprint arXiv:2105.04026 78, 3, 2021 | 269 | 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 | 238 | 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 | 163 | 2022 |
| Phase retrieval: uniqueness and stability P Grohs, S Koppensteiner, M Rathmair SIAM Review 62 (2), 301-350, 2020 | 120 | 2020 |
| Mathematical aspects of deep learning P Grohs, G Kutyniok Cambridge University Press, 2022 | 106 | 2022 |
| Stable phase retrieval in infinite dimensions R Alaifari, I Daubechies, P Grohs, R Yin Foundations of Computational Mathematics 19 (4), 869-900, 2019 | 93 | 2019 |
| Laguerre minimal surfaces, isotropic geometry and linear elasticity H Pottmann, P Grohs, NJ Mitra Advances in computational mathematics 31 (4), 391, 2009 | 93 | 2009 |
| ε-subgradient algorithms for locally lipschitz functions on Riemannian manifolds P Grohs, S Hosseini Advances in Computational Mathematics 42 (2), 333-360, 2016 | 92 | 2016 |
| 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 | 88 | 2017 |
| 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 | 86 | 2022 |
| Parabolic molecules P Grohs, G Kutyniok Foundations of Computational Mathematics 14 (2), 299-337, 2014 | 86 | 2014 |
| 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 | 85 | 2021 |
| 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 | 83 | 2020 |
| Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neural network approximation spaces P Grohs, F Voigtlaender Foundations of Computational Mathematics 24 (4), 1085-1143, 2024 | 80 | 2024 |
| Stable Gabor phase retrieval and spectral clustering P Grohs, M Rathmair Communications on Pure and Applied Mathematics 72 (5), 981-1043, 2019 | 75 | 2019 |
| 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 | 74 | 2022 |
