Publications
- 2024
- G. M. R. Costa and M. Ehrhardt, "Mathematical analysis and a nonstandard scheme for a model of the immune response against COVID-19" in Mathematical and Computational Modeling of Phenomena Arising in Population Biology and Nonlinear Oscillations, AMS Contemporary Mathematics, 2024, pp. 251–270.
- T. Kruse and P. Strack, "Optimal dynamic control of an epidemic", Operations Research, vol. 72, no. 3, pp. 1031–1048, 2024. INFORMS.
- A. Bartel, M. Clemens, M. Günther, B. Jacob and T. Reis, "Port-Hamiltonian systems’ modelling in electrical engineering" in Scientific Computing in Electrical Engineering: SCEE 2022, Amsterdam, The Netherlands, July 2022, van Beurden, Martijn and Budko, Neil V. and Ciuprina, Gabriela and Schilders, Wil and Bansal, Harshit and Barbulescu, Ruxandra, Eds. Springer Cham, 2024, pp. 133–143.
- J. Ackermann, T. Kruse and M. Urusov, "Reducing Obizhaeva-Wang-type trade execution problems to LQ stochastic control problems", Finance and Stochastics, vol. 28, no. 3, pp. 813–863, 2024. Springer Verlag.
- J. Ackermann, T. Kruse and M. Urusov, "Self-exciting price impact via negative resilience in stochastic order books", Annals of Operations Research, vol. 336, no. 1, pp. 637–659, 2024. Springer Netherlands.
- A. Bartel, M. Diab, A. Frommer, M. Günther and N. Marheineke, "Splitting Techniques for DAEs with port-Hamiltonian Applications", Preprint, 2024.
- J. Ackermann, M. Ehrhardt, T. Kruse and A. Tordeux, "Stabilisation of stochastic single-file dynamics using port-Hamiltonian systems", Preprint, 2024.
- M. Clemens, M. Henkel, F. Kasolis and M. Günther, "Structural Aspects of Electromagneto-Quasistatic Field Formulations of Darwin-Type Derived in the Port-Hamiltonian System Framework", TechRxiv, 2024. IEEE.
- M. Günther, B. Jacob and C. Totzeck, "Structure-Preserving Identification of Port-Hamiltonian Systems—A Sensitivity-Based Approach" in Scientific Computing in Electrical Engineering SCEE 2022, Amsterdam, The Netherlands, July 2022, van Beurden, Martijn and Budko, Neil V. and Ciuprina, Gabriela and Schilders, Wil and Bansal, Harshit and Barbulescu, Ruxandra, Eds. Springer Cham, 2024, pp. 167–174.
- M. Ehrhardt, T. Kruse and A. Tordeux, "The collective dynamics of a stochastic port-Hamiltonian self-driven agent model in one dimension", ESAIM: Mathematical Modelling and Numerical Analysis, vol. 58, no. 2, pp. 515–544, 2024. EDP Sciences.
- L. Kapllani, L. Teng and M. Rottmann, "Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations", Preprint IMACM, 2024. Bergische Universität Wuppertal.
- 2023
- M. Ehrhardt, "3D Modeling of sound field hologram of moving source in presence of internal waves causing horizontal refraction.", Preprint IMACM, 2023. Bergische Universität Wuppertal.
- F. Klass, A. Gabbana and A. Bartel, "A characteristic boundary condition for multispeed lattice Boltzmann methods", Communications in Computational Physics, vol. 33, no. 1, pp. 101–117, 2023. Global Science Press.
- H. Fatoorehchi and M. Ehrhardt, "A combined method for stability analysis of linear time invariant control systems based on Hermite-Fujiwara matrix and Cholesky decomposition", The Canadian Journal of Chemical Engineering, vol. 101, no. 12, pp. 7043–7052, 2023. John Wiley & Sons.
- H. Fatoorehchi and M. Ehrhardt, "A new method for stability analysis of linear time-invariant systems and continuous-time nonlinear systems with application to process dynamics and control", Preprint IMACM, 2023.
- T. Schäfers and L. Teng, "Asymmetry in stochastic volatility models with threshold and time-dependent correlation", Studies in Nonlinear Dynamics & Econometrics, vol. 27, no. 2, pp. 131–146, 2023. De Gruyter.
- T. Kossaczká, M. Ehrhardt and M. Günther, "Deep FDM: Enhanced finite difference methods by deep learning", Franklin Open, vol. 4, pp. 100039, 2023. Elsevier.
- T. Kossaczká, M. Ehrhardt and M. Günther, "Deep finite difference method for solving Asian option pricing problems", Preprint IMACM, 2023. Bergische Universität Wuppertal.
- J. Ackermann, A. Jentzen, T. Kruse, B. Kuckuck and J. L. Padgett, "Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the Lp-sense", Preprint, 2023.
- M. Ehrhardt and K. S. Matyokubov, "Driven transparent quantum graphs", Preprint, 2023.
