Publikationen Prof. Dr. Matthias Ehrhardt
- 2022
- M. Ehrhardt and M. Günther, "A neural network enhanced weighted essentially non-oscillatory method for nonlinear degenerate parabolic equations", Physics of Fluids, vol. 34, no. 2, pp. 026604, 2022. AIP Publishing.
- F. Klass, A. Gabbana and A. Bartel, "A non-reflecting boundary condition for multispeed lattice Boltzmann methods" in Progress in Industrial Mathematics at ECMI 2021, Ehrhardt, Matthias and Günther, Michael, Eds. Springer Cham, 2022, pp. 447–453.
- S. Treibert, H. Brunner and M. Ehrhardt, "A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics", Mathematical Biosciences and Engineering, vol. 19, no. 2, pp. 1213–1238, 2022. AIMS Press.
- M. Ehrhardt, "A physics-informed neural network to model COVID-19 infection and hospitalization scenarios", Advances in continuous and discrete models, vol. 2022, no. 1, pp. 1–27, 2022. Springer Science and Business Media Deutschland GmbH.
- J. Jäschke, M. Ehrhardt, M. Günther and B. Jacob, "A port-Hamiltonian formulation of coupled heat transfer", Mathematical and Computer Modelling of Dynamical Systems, vol. 28, no. 1, pp. 78–94, 2022. Taylor & Francis.
- J. Jäschke, M. Ehrhardt, M. Günther and B. Jacob, "A two-dimensional port-Hamiltonian model for coupled heat transfer", Mathematics, vol. 10, no. 24, pp. 4635, 2022. MDPI.
- M. Ehrhardt, "An efficient second-order method for the linearized Benjamin-Bona-Mahony equation with artificial boundary conditions", Preprint IMACM, 2022. Bergische Universität Wuppertal.
- M. Muniz, M. Ehrhardt and M. Günther, "Correlation matrices driven by stochastic isospectral flows" in Progress in Industrial Mathematics at ECMI 2021, Springer Cham, 2022, pp. 455–461.
- J. Jäschke, M. Ehrhardt, M. Günther and B. Jacob, "Discrete port-Hamiltonian coupled heat transfer" in Progress in Industrial Mathematics at ECMI 2021, Ehrhardt, Matthias and Günther, Michael, Eds. Springer Cham, 2022, pp. 439–445.
- M. Muniz, M. Ehrhardt, M. Günther and R. Winkler, "Higher strong order methods for linear Itô SDEs on matrix Lie groups", BIT Numerical Mathematics, vol. 62, no. 3, pp. 1095–1119, 2022. Springer Netherlands.
- A. Bartel and M. Ehrhardt, "Lagrangian instabilities in thermal convection with stable temperature profiles", Preprint IMACM, 2022. Bergische Universität Wuppertal.
- A. Bartel and M. Ehrhardt, "Large-scale convective flow sustained by thermally active Lagrangian tracers", Journal of Fluid Mechanics, vol. 953, pp. A5, 2022. Cambridge University Press.
- H. Fatoorehchi and M. Ehrhardt, "Numerical and semi-numerical solutions of a modified Thévenin model for calculating terminal voltage of battery cells", Journal of Energy Storage, vol. 45, pp. 103746, 2022. Elsevier.
- M. Ehrhardt, "On decomposition of the fundamental solution of the Helmholtz equation over solutions of iterative parabolic equations", Asymptotic Analysis, vol. 126, no. 3-4, pp. 215–228, 2022. IOS Press.
- Progress in Industrial Mathematics at ECMI 2021. Springer Cham, 2022.
ISBN: 978-3-031-11817-3
- M. Muniz, M. Ehrhardt, M. Günther and R. Winkler, "Stochastic Runge-Kutta-Munthe-Kaas methods in the modelling of perturbed rigid bodies", Advances in Applied Mathematics and Mechanics, vol. 14, no. 2, pp. 528–538, 2022. Global Science Press.
- A. Clevenhaus, M. Ehrhardt and M. Günther, "The parareal algorithm and the sparse grid combination technique in the application of the Heston model" in Progress in Industrial Mathematics at ECMI 2021, Springer Cham, 2022, pp. 477–483.
- M. Ehrhardt, "Transparent boundary conditions for the sine-Gordon equation: Modeling the reflectionless propagation of kink solitons on a line", Physics Letters, Section A, vol. 423, pp. 127822, 2022. Elsevier.
- 2021
- A. Clevenhaus, C. Totzeck and M. Ehrhardt, "A gradient descent algorithm for the Heston model", Preprint IMACM, 2021. Bergische Universität Wuppertal.
- L. Kapllani, L. Teng and M. Ehrhardt, "A multistep scheme to solve backward stochastic differential equations for option pricing on GPUs" in Advances in High Performance Computing: Results of the International Conference on “High Performance Computing” Borovets, Bulgaria, 2019, Dimov, Ivan and Fidanova, Stefka, Eds. Springer Cham, 2021, pp. 196–208.
- A. Clevenhaus, M. Ehrhardt and M. Günther, "A parallel sparse grid combination technique using the Parareal Algorithm", Preprint IMACM, 2021. Bergische Universität Wuppertal.
- A. Clevenhaus, M. Ehrhardt and M. Günther, "An ADI Sparse Grid method for pricing efficiently American options under the Heston model", Advances in Applied Mathematics and Mechanics, vol. 13, no. 6, pp. 1384–1397, 2021. Global Science Press.
- M. Muniz, M. Ehrhardt and M. Günther, "Approximating correlation matrices using stochastic Lie group methods", Mathematics, vol. 9, no. 1, pp. 94, 2021. MDPI.
- E. Viviani, L. Di Persio and M. Ehrhardt, "Energy markets forecasting. From inferential statistics to machine learning: The German case", Energies, vol. 14, no. 2, pp. 364, 2021. MDPI.
- T. Kossaczká, M. Ehrhardt and M. Günther, "Enhanced fifth order WENO shock-capturing schemes with deep learning", Results in Applied Mathematics, vol. 12, pp. 100201, 2021. Elsevier.
