Besucheranschrift
Helmut-Schmidt-Universität
Gebäude H1
Holstenhofweg 85
22043 Hamburg
Postanschrift
Helmut-Schmidt-Universität
Fakultät für Maschinenbau
Numerische Mathematik
Postfach 70 08 22
22008 Hamburg
Publikationen
- N. Margenberg, R. Jendersie, C. Lessig, T. Richter, DNN-MG: A hybrid neural network/finite element method with applications to 3D simulations of the Navier–Stokes equations, 2024, Comput. Methods Appl. Mech. Engrg. 420, 116692. https://doi.org/10.1016/j.cma.2023.116692
- N. Margenberg, Franz X. Kärtner, M. Bause, Accurate simulation of THz generation with Finite-Element Time Domain methods, Opt. Express, accepted (2023), pp. 1-23, https://doi.org/10.1364/OE.480793; arXiv:2211.06854.
- N. Margenberg, F. X. Kärtner, M. Bause, Optimal Dirichlet Boundary Control by Fourier Neural Operators Applied to Nonlinear Optics, J. Comput. Phys., accepted (2023), pp. 1-43; https://doi.org/10.1016/j.jcp.2023.112725.
- M. Anselmann, M. Bause, N. Margenberg, P. Shamko, Benchmark computations of dynamic poroelasticity, Proc. Appl. Math. Mech., submitted (2023), pp. 1-6; arXiv:2307.02057.
- M. Anselmann, M. Bause, N. Margenberg, P. Shamko, An energy-efficient GMRES-Multigrid solver for space-time finite element computation of dynamic poro- and thermoelasticity, Comput. Mech., submitted (2023), pp. 1-30; arXiv:2303.06742.
- N. Margenberg, D. Hartmann, C. Lessig, T. Richter, A neural network multigrid solver for the Navier-Stokes equations, J. Comput. Phys., 2022, https://doi.org/10.1016/j.jcp.2022.110983
- N. Margenberg, C. Lessig, T. Richter, Structure preservation for the Deep Neural Network Multigrid Solver, Electron. Trans. Numer. Anal., 2022, https://doi.org/10.1553/etna_vol56s86
- N. Margenberg, T. Richter, Parallel time-stepping for fluid–structure interactions, Math. Model. Nat. Phenom., 2021, https://doi.org/10.1051/mmnp/2021005
Letzte Änderung: 29. Oktober 2024