| DNN-MG: A hybrid neural network/finite element method with applications to 3D simulations of the NavierStokes equations N Margenberg, R Jendersie, C Lessig, T Richter Computer Methods in Applied Mechanics and Engineering 420, 116692, 2024 | 6 | 2024 |
| Deep neural networks for geometric multigrid methods N Margenberg, R Jendersie, T Richter, C Lessig arXiv preprint arXiv:2106.07687, 2021 | 3 | 2021 |
| NeuroPNM: Model reduction of pore network models using neural networks R Jendersie, A Mjalled, X Lu, L Reineking, A Kharaghani, M Mönnigmann, ... Particuology 86, 239-251, 2024 | 2 | 2024 |
| The neural network multigrid solver for the Navier-Stokes equations and its application to 3D simulation N Margenberg, R Jendersie, CL Lessig, TR Richter ECCOMAS Congress 2022-8th European Congress on Computational Methods in
, 0 | 1 | |
| A GPU-parallelization of the neXtSIM-DG dynamical core (v0. 3.1) R Jendersie, C Lessig, T Richter EGUsphere 2024, 1-32, 2024 | | 2024 |
| Towards a GPU-Parallelization of the neXtSIM-DG Dynamical Core R Jendersie, C Lessig, T Richter Proceedings of the Platform for Advanced Scientific Computing Conference, 1-10, 2024 | | 2024 |
| A comparison of numerical methods for model reduction of dense discrete-time systems R Jendersie, SWR Werner at-Automatisierungstechnik 69 (8), 683-694, 2021 | | 2021 |
| Hybrid Finite Element/Neural Network Simulations R Jendersie, U Kapustsin, U Kaya, C Lessig, N Margenberg, D Hartmann | | |
