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)