A. Clevenhaus, M. Ehrhardt and M. Günther, "An ADI Sparse Grid Method for Pricing Efficiently American Options under the Heston Model" , AAMM, vol. 13, no. 6, pp. 1384--1397, 2021.

J. Jäschke, M. Ehrhardt, M. Günther and B. Jacob, "A Two-Dimensional Port-{Hamiltonian} Model for Coupled Heat Transfer" , IMACM preprint 21/28, 2021.

L. Teng, "A review of tree-based approaches to solve forward-backward stochastic differential equations" , JCF, vol. 25, no. 3, pp. 125--159, 2021.

A. Clevenhaus, M. Ehrhardt and M. Günther, "A parallel Sparse Grid Combination Technique using the {Parareal} Algorithm" , IMACM preprint 21/18, 2021.

F. Klass, A. Gabbana and A. Bartel, "A non-equilibrium bounce-back boundary condition for thermal multispeed {LBM}" , J. Comput. Sci., vol. 53, pp. 101364, 2021. Elsevier {BV}.

{Rosenbrock}-{Wanner}-Type Methods: Theory and Applications. .... Springer, 2021.

M. Muniz, M. Ehrhardt and M. Günther, "Approximating Correlation Matrices Using Stochastic {Lie} Group Methods" , Mathematics, vol. 9, no. 1, pp. 94, Jan. 2021. MDPI AG.

J. Ackermann, T. Kruse and M. Urusov, "C{\`a}dl{\`a}g semimartingale strategies for optimal trade execution in stochastic order book models" , Finance and Stochastics, vol. 25, no. 4, pp. 757--810, 2021. Springer Berlin Heidelberg.

M. Günther, A. Bartel, B. Jacob and T. Reis, "Dynamic iteration schemes and port-Hamiltonian formulation in coupled {DAE} circuit simulation" , Int. J. Circ. Theo. Appl., vol. 49, pp. 430-452, 2021.

M. Felpel, J. Kienitz and T. A. McWalter, "Effective stochastic volatility: Applications to {ZABR}-type models" , Quantitative Finance, vol. 21, no. 5, pp. 837-852, 2021. Routledge.

T. Kruse and P. Strack, "Optimal control of an epidemic through social distancing" , Available at SSRN 3581295, 2020.

A. Clevenhaus, M. Ehrhardt, M. Günther and D. Ševčovič, "Pricing {American} Options with a Non-Constant Penalty Parameter" , J. Risk Fin. Managem., vol. 13, no. 6, pp. 124, 2020.

M. Hutzenthaler, A. Jentzen, T. Kruse, T. Anh Nguyen and P. Wurstemberger, "Overcoming the curse of dimensionality in the numerical approximation of semilinear parabolic partial differential equations" , Proceedings of the Royal Society A, vol. 476, no. 2244, pp. 20190630, 2020. The Royal Society Publishing.

C. Beck, F. Hornung, M. Hutzenthaler, A. Jentzen and T. Kruse, "Overcoming the curse of dimensionality in the numerical approximation of Allen--Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations" , Journal of Numerical Mathematics, vol. 28, no. 4, pp. 197--222, 2020. De Gruyter.

S. Ankirchner, A. Fromm, T. Kruse and A. Popier, "Optimal position targeting via decoupling fields" , The Annals of Applied Probability, vol. 30, no. 2, pp. 644--672, 2020. Institute of Mathematical Statistics.

C. Beck, A. Jentzen and T. Kruse, "Nonlinear Monte Carlo methods with polynomial runtime for high-dimensional iterated nested expectations" , arXiv preprint arXiv:2009.13989, 2020.

L. Teng, X. Wu, M. Günther and M. Ehrhardt, "A new methodology to create valid time-dependent correlation matrices via isospectral flows" , {ESAIM}: Mathematical Modelling and Numerical Analysis, vol. 54, no. 2, pp. 361--371, 2020. {EDP} Sciences.

M. Hutzenthaler, A. Jentzen, T. Kruse and T. A. Nguyen, "Multilevel Picard approximations for high-dimensional semilinear second-order PDEs with Lipschitz nonlinearities" , arXiv preprint arXiv:2009.02484, 2020.

A. Bartel and M. Günther, "Inter/extrapolation-based multirate schemes -- a dynamic-iteration perspective", Reis, Timo and Grundel, Sara and Schöps, Sebastian, Eds. Springer, 2020, pp. 73--90.

L. Kapllani and L. Teng, "Deep Learning algorithms for solving high dimensional nonlinear Backward Stochastic Differential Equations" , arXiv preprint arXiv:2010.01319, 2020.

M. Günther, R. Höllwieser and F. Knechtli, "Constrained hybrid Monte Carlo algorithms for gauge{\textendash}Higgs models" , Comput. Phys. Commun., vol. 254, pp. 107192, 2020. Elsevier {BV}.

T. Kruse, J. C. Schneider and N. Schweizer, "A toolkit for robust risk assessment using F-divergences" , Management Science, 2020. INFORMS Inst. for Operations Res. and the Management Sciences.

J. Kühn, A. Bartel and P. Putek, "A Thermal Extension of Tellinen's Scalar Hysteresis Model" in Scientific Computing in Electrical Engineering SCEE 2018, Springer, Berlin, 2020.

M. Hutzenthaler, A. Jentzen, T. Kruse and T. A. Nguyen, "A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations" , SN Partial Differential Equations and Applications, vol. 1, no. 2, pp. 1--34, 2020. Springer International Publishing.

M. Hutzenthaler and T. Kruse, "Multilevel Picard Approximations of High-Dimensional Semilinear Parabolic Differential Equations with Gradient-Dependent Nonlinearities" , SIAM Journal on Numerical Analysis, vol. 58, no. 2, pp. 929--961, 2020. SIAM.