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