Preprints 2023

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

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

    arXiv

     

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

    Asymptotic Analysis for Optimal Control of the Cattaneo Model

    arXiv

     

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

    Detecting Novelties with Empty Classes

    arXiv

     

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

    Augmentation-based Domain Generalization for Semantic Segmentation

    arXiv
  • 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

    arXiv
  • 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

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

    Operator splitting for port-Hamiltonian systems

    arXiv
  • 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

    arXiv
  • 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

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

    Modeling Minimum Cost Network Flows With Port-Hamiltonian Systems

    arXiv
  • Frommer, A. and Khalil, M. N.

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

    arXiv
  • Glück, J. and Hölz, J.

    Eventual cone invariance revisited

    arXiv
  • 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

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

    Input-to-state stability for bilinear feedback systems

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

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

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

    Non-convex shape optimization by dissipative Hamiltonian flows

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

    LU-Net: Invertible Neural Networks Based on Matrix Factorization

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

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

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

    Ridepooling and public bus services: A comparative case-study

    arXiv
  • 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

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

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

    arXiv
  • Bauß, J. and Stiglmayr, M.

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

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

    On the construction and identifcation of Boltzmann processes

    arXiv
  • Jacob, B. and Totzeck, C.

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

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

    Stability properties of some port-Hamiltonian SPDEs

    arXiv
  • Mandrekar, V. and Rüdiger, B.

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

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

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

    arXiv
  • Krüger, P. and Gottschalk, H.

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

    arXiv
  • Tordeux, A. and Totzeck, C.

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

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

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

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

    Port-Hamiltonian Systems Modelling in Electrical Engineering

    arXiv
  • 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: 17.05.2023

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