Preprints 2023

  • Kossaczká, T. and Jagtap, A. D. and Ehrhardt, M.

    Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators

    Preprint: imacm_23_14 download


  • Pereselkov, S. and Kuz’kin, V. and Ehrhardt, M. and Tkachenko, S. and Rybyanets, P. and Ladykin, N.

    3D Modeling of Sound Field Hologram of Moving Source in Presence of Internal Waves Causing Horizontal Refraction

    Preprint: imacm_23_13 download


  • Maamar, H.M. and Ehrhardt, M. and Tabharit, L.

    A Nonstandard Finite Difference Scheme for a Time-Fractional Model of Zika Virus Transmission

    Preprint: imacm_23_12 download


  • Abdellatif, M. and Kuchling, P. and Rüdiger, B. and Ventura, I.

    Wasserstein distance in terms of the Comonotonicity Copula



  • Clevenhaus, A. and Totzeck, C. and Ehrhardt, M.

    A numerical study of the impact of variance boundary conditions for the Heston model

    Preprint: imacm_23_11 download


  • Klass, F. and Gabbana, A. and Bartel, A.

    Characteristic Boundary Condition for Thermal Lattice Boltzmann Methods

    Preprint: imacm_23_10 download


  • Hoang, M.T. and Ehrhardt, M.

    A general class of second-order L-stable explicit numerical methods for stiff problems

    Preprint: imacm_23_09 download


  • Hoang, M.T. and Ehrhardt, M.

    A dynamically consistent nonstandard finite difference scheme for a generalized SEIR epidemic model

    Preprint: imacm_23_08 download


  • Hoang, M.T. and Ehrhardt, M.

    A second-order nonstandard finite difference method for a general Rosenzweig-MacArthur predator-prey model

    Preprint: imacm_23_07 download


  • Gernandt, H. and Philipp, F. and Preuster, T. and Schaller, M.

    On the equivalence of geometric and descriptor representations of linear port-Hamiltonian systems



  • Blauth, S. and Pinnau, R. and Andres, M. and Totzeck, C.

    Asymptotic Analysis for Optimal Control of the Cattaneo Model



  • Uhlemeyer, S. and Lienen, J. and Hüllermeier, E. and Gottschalk, H.

    Detecting Novelties with Empty Classes



  • Schwonberg, M. and El Bouazati, F. and Schmidt, N. M. and Gottschalk, H.

    Augmentation-based Domain Generalization for Semantic Segmentation



  • Schwonberg, M. and Niemeijer, J. and Termöhlen, J.-A. and Schäfer, J. P. and Schmidt, N. M. and Gottschalk, H. and Fingscheidt, T.

    Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving



  • Burghoff, J. and Monells, M. H. and Gottschalk, H.

    Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics



  • Frommer, A. and Günther, M. and Liljegren-Sailer, B. and Marheineke, N.

    Operator splitting for port-Hamiltonian systems



  • Duan, H. and Shen, W. and Min, X. and Tu, D. and Teng, L. and Wang, J. and Zhai, G.

    Masked Autoencoders as Image Processors



  • Doganay, O. T. and Klamroth, K. and Lang, B. and Stiglmayr, M. and Totzeck, C.

    Optimal control for port-Hamiltonian systems and a new perspective on dynamic network flow problems



  • Doganay, O. T. and Klamroth, K. and Lang, B. and Stiglmayr, M. and Totzeck, C.

    Modeling Minimum Cost Network Flows With Port-Hamiltonian Systems



  • Frommer, A. and Khalil, M. N.

    MG-MLMC++ as a Variance Reduction Method for Estimating the Trace of a Matrix Inverse



  • Glück, J. and Hölz, J.

    Eventual cone invariance revisited



  • Schubert, M. and Riedlinger, T. and Kahl, K. and Kröll, D. and Schoenen, S. and Šegvić, S. and Rottmann, M.

    Identifying Label Errors in Object Detection Datasets by Loss Inspection



  • Hosfeld, R. and Jacob, B. and Schwenninger, F. and Tucsnak, M.

    Input-to-state stability for bilinear feedback systems



  • Schillings, C. and Totzeck, C. and Wacker, P.

    Ensemble-based gradient inference for particle methods in optimization and sampling



  • Bolten, M. and Doganay, O. T. and Gottschalk, H. and Klamroth, K.

    Non-convex shape optimization by dissipative Hamiltonian flows



  • Chan, R. and Penquitt, S. and Gottschalk, H.

    LU-Net: Invertible Neural Networks Based on Matrix Factorization



  • Drygala, C. and di Mare, F. and Gottschalk, H.

    Generalization capabilities of conditional GAN for turbulent flow under changes of geometry



  • Asatryan, H. and Gaul, D. and Gottschalk, H. and Klamroth, K. and Stiglmayr, M.

    Ridepooling and public bus services: A comparative case-study



  • Heldmann, F. and Berkhahn, S. and Ehrhardt, M. and Klamroth, K.

    PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss



  • Farkas, B. and Jacob, B. and Reis, T. and Schmitz, M.

    Operator splitting based dynamic iteration for linear infinite-dimensional port-Hamiltonian systems



  • Bauß, J. and Stiglmayr, M.

    Augmenting Bi-objective Branch and Bound by Scalarization-Based Information



  • Albeverio, S. and Rüdiger, B. and Sundar, P.

    On the construction and identifcation of Boltzmann processes



  • Jacob, B. and Totzeck, C.

    Port-Hamiltonian structure of interacting particle systems and its mean-field limit



  • Kuchling, P. and Rüdiger, B. and Ugurcan, B.

    Stability properties of some port-Hamiltonian SPDEs



  • Mandrekar, V. and Rüdiger, B.

    Stability properties of mild solutions of SPDEs related to pseudo differential equations



  • Günther, M. and Jacob, B. and Totzeck, C.

    Data-driven adjoint-based calibration of port-Hamiltonian systems in time domain



  • Krüger, P. and Gottschalk, H.

    Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group



  • Tordeux, A. and Totzeck, C.

    Multi-scale description of pedestrian collective dynamics with port-Hamiltonian systems



  • Günther, M. and Jacob, B. and Totzeck, C.

    Structure-preserving identification of port-Hamiltonian systems -- a sensitivity-based approach



  • Bartel, A. and Clemens, M. and Günther, M. and Jacob, B. and Reis, T.

    Port-Hamiltonian Systems Modelling in Electrical Engineering



  • Kossaczká, T. and Ehrhardt, M. and Günther, M.

    Deep FDM: Enhanced finite difference methods by deep learning

    Preprint: imacm_23_06 download


  • Ehrhardt, M. and Kruse, T. and Tordeux, A.

    The Collective Dynamics of a Stochastic Port-Hamiltonian Self-Driven Agent Model in One Dimension

    Preprint: imacm_23_05 download / arXiv


  • Beck, C. and Jentzen, A. and Kleinberg, K. and Kruse T.

    Nonlinear Monte Carlo methods with polynomial runtime for Bellman equations of discrete time high-dimensional stochastic optimal control problems

    Preprint: imacm_23_04 download / arXiv


  • Pereselkov, S and Kuz’kin, V. and Ehrhardt, M. and Tkachenko, S. and Rybyanets, R.

    Use of Interference Patterns to Control Sound Field Focusing in Shallow Water

    Preprint: imacm_23_03 download


  • Morais Rodrigues Costa, G. and Lobosco, M. and Ehrhardt, M. and Reis, R.F.

    Mathematical Analysis and a Nonstandard Scheme for a Model of the Immune Response against COVID-19

    Preprint: imacm_23_02 download


  • Fatoorehchi, H. and Zarghami, R. and Ehrhardt, M.

    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_23_01 (Submitted to The Canadian Journal of Chemical Engineering)

zuletzt bearbeitet am: 18.09.2023

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