| Mutational hierarchies in myelodysplastic syndromes dynamically adapt and evolve upon therapy response and failure M Mossner, JC Jann, J Wittig, F Nolte, S Fey, V Nowak, J Obländer, ... Blood, The Journal of the American Society of Hematology 128 (9), 1246-1259, 2016 | 135 | 2016 |
| Tensor-based dynamic mode decomposition S Klus, P Gelß, S Peitz, C Schütte Nonlinearity 31 (7), 3359, 2018 | 92 | 2018 |
| Multidimensional approximation of nonlinear dynamical systems P Gelß, S Klus, J Eisert, C Schütte Journal of Computational and Nonlinear Dynamics 14 (6), 061006, 2019 | 79 | 2019 |
| Solving the master equation without kinetic Monte Carlo: Tensor train approximations for a CO oxidation model P Gelß, S Matera, C Schütte Journal of Computational Physics 314, 489-502, 2016 | 39 | 2016 |
| Tensor-based algorithms for image classification S Klus, P Gelß Algorithms 12 (11), 240, 2019 | 35 | 2019 |
| The tensor-train format and its applications: Modeling and analysis of chemical reaction networks, catalytic processes, fluid flows, and Brownian dynamics P Gelß | 27 | 2017 |
| Nearest-neighbor interaction systems in the tensor-train format P Gelß, S Klus, S Matera, C Schütte Journal of Computational Physics 341, 140-162, 2017 | 25 | 2017 |
| The tensor-train format and its applications P Gelß PhD thesis, 2017 | 17 | 2017 |
| Tensor-based computation of metastable and coherent sets F Nüske, P Gelß, S Klus, C Clementi Physica D: Nonlinear Phenomena 427, 133018, 2021 | 14 | 2021 |
| Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry S Klus, P Gelß, F Nüske, F Noé Machine Learning: Science and Technology 2 (4), 045016, 2021 | 14 | 2021 |
| Solving the time-independent Schrödinger equation for chains of coupled excitons and phonons using tensor trains P Gelß, R Klein, S Matera, B Schmidt The Journal of Chemical Physics 156 (2), 2022 | 10 | 2022 |
| Improved local models and new Bell inequalities via Frank-Wolfe algorithms S Designolle, G Iommazzo, M Besançon, S Knebel, P Gelß, S Pokutta Physical Review Research 5 (4), 043059, 2023 | 7 | 2023 |
| Feature space approximation for kernel-based supervised learning P Gelß, S Klus, I Schuster, C Schütte Knowledge-Based Systems 221, 106935, 2021 | 6 | 2021 |
| Tensor-based EDMD for the Koopman analysis of high-dimensional systems F Nüske, P Gelß, S Klus, C Clementi arXiv preprint arXiv:1908.04741, 210, 2019 | 4 | 2019 |
| Low-rank tensor decompositions of quantum circuits P Gelß, S Klus, S Knebel, Z Shakibaei, S Pokutta arXiv preprint arXiv:2205.09882, 2022 | 3 | 2022 |
| Tensor-generated fractals: Using tensor decompositions for creating self-similar patterns P Gelß, C Schütte arXiv preprint arXiv:1812.00814, 2018 | 3 | 2018 |
| Low-rank approximability of nearest neighbor interaction systems P Gelß, S Matera, R Schneider, A Uschmajew | 2 | 2018 |
| WaveTrain: A Python package for numerical quantum mechanics of chain-like systems based on tensor trains J Riedel, P Gelß, R Klein, B Schmidt The Journal of Chemical Physics 158 (16), 2023 | 1 | 2023 |
| Fredholm integral equations for function approximation and the training of neural networks P Gelß, A Issagali, R Kornhuber arXiv preprint arXiv:2303.05262, 2023 | 1 | 2023 |
| Quantum dynamics of coupled excitons and phonons in chain-like systems: tensor train approaches and higher-order propagators P Gelß, S Matera, R Klein, B Schmidt arXiv preprint arXiv:2302.03568, 2023 | 1 | 2023 |
