Publikationen des Jahres 2023

Zeitschriftenartikel (26)

1. F. Altenkrüger, A. Denker, P. Hagemann, P. Maaß, G. Steidl.
   PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
   Inverse Problems, 39(6), 2023.

   online unter: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

2. J. Antorán, R. Barbano, J. Leuschner, J. M. Hernández-Lobato, B. Jin.
   Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.
   Transactions on Machine Learning Research, 12, 2023.

   online unter: https://openreview.net/forum?id=FWyabz82fH

3. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
   Invertible residual networks in the context of regularization theory for linear inverse problems.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

4. C. Arndt, A. Denker, S. Dittmer, J. Leuschner, J. Nickel, M. Schmidt.
   Model-based deep learning approaches to the Helsinki Tomography Challenge 2022.
   Applied Mathematics for Modern Challenges, 1(2), 2023.

   DOI: 10.3934/ammc.2023007

5. S. Banert, P. Giselsson, H. Sadeghi.
   Incorporating history and deviations in forward–backward splitting.
   Numerical Algorithms, , 2023.

   DOI: 10.1007/s11075-023-01686-8

6. S. Banert, P. Giselsson, M. Morin.
   Nonlinear Forward-Backward Splitting with Momentum Correction.
   Set-Valued and Variational Analysis, 31(37), 2023.

   DOI: 10.1007/s11228-023-00700-4

7. S. Banert, O. Öktem, J. Adler, J. Rudzusika.
   Accelerated Forward-Backward Optimization using Deep Learning.
   Erscheint in SIAM Journal on Optimization

   online unter: https://arxiv.org/abs/2105.05210

8. N. K. Bellam Muralidhar, C. Gräßle, N. Rauter, A. Mikhaylenko, R. Lammering, D. Lorenz.
   Damage identification in fiber metal laminates using bayesian analysis with model order reduction.
   Computer Methods in Applied Mechanics and Engineering, Part B 403, 2023.

   DOI: 10.1016/j.cma.2022.115737
   online unter: https://arxiv.org/abs/2206.04329

9. A. Denker, I. Singh, R. Barbano, Z. Kereta, B. Jin, K. Thielemans, P. Maaß, S. Arridge.
   Score-Based Generative Models for PET Image Reconstruction.
   Erscheint in Machine Learning for Biomedical Imaging

   online unter: https://arxiv.org/abs/2308.14190

10. E. Dierkes, C. Offen, S. Ober-Blöbaum, K. Flaßkamp.
    Hamiltonian neural networks with automatic symmetry detection.
    Chaos: an Interdisciplinary Journal of Nonlinear Science, 33(6), 2023.

    DOI: 10.1063/5.0142969

11. S. Dittmer, M. Roberts, J. Gilbey, A. Biguri, .. AIX-COVNET Collaboration, J. Preller, J. H. F. Rudd, J. A. D. Aston, C. Schönlieb.
    Navigating the development challenges in creating complex data systems.
    nature machine intelligence, 5:681-686, Springer Verlag, 2023.

    DOI: 10.1038/s42256-023-00665-x
    online unter: https://www.nature.com/articles/s42256-023-00665-x#citeas

12. A. Ebner, J. Frikel, D. Lorenz, J. Schwab, M. Haltmeier.
    Regularization of inverse problems by filtered diagonal frame decomposition.
    Applied and Computational Harmonic Analysis, 62:66-83, 2023.

    DOI: 10.1016/j.acha.2022.08.005
    online unter: https://arxiv.org/abs/2008.06219

13. D. Erzmann, S. Dittmer, H. Harms, P. Maaß.
    DL4TO: A Deep Learning Library for Sample-Efficient Topology Optimization.
    Lecture Notes in Computer Science, Geometric Science of Information. GSI 2023 14071, Springer Verlag, 2023.

    DOI: 10.1007/978-3-031-38271-0_54

14. M. Flatken, A. Podobas, R. Fellegara, A. Basermann, J. Holke, D. Knapp, M. Nolde, C. Krullikowski, N. Brown, R. Nash, E. Belikov, S. W. D. Chien, S. Markidis, J. Tierny, J. Vidal, C. Gueunet, J. Guenther, P. Poletti, G. Guzzetta, M. Manica, A. . Zardini, J. Chaboureau, M. Mendes, A. Cardil, S. Monedero, J. Ramirez, A. Gerndt.
    VESTEC: Visual Exploration and Sampling Toolkit for Extreme Computing. Urgent decision making meets HPC: Experiences and Future Challenges.
    IEEE Access Journal, Vol. 11, pp. 87805-87834 , 2023.

    online unter: https://elib.dlr.de/200273/

15. J. Gödeke, G. Rigaud.
    Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods.
    Inverse Problems, 39(3), 2023.

    DOI: 10.1088/1361-6420/acb2ed

16. R. Gower, D. Lorenz, M. Winkler.
    A Bregman-Kaczmarz method for nonlinear systems of equations.
    Computational Optimization and Applications, , 2023.

    DOI: 10.1007/s10589-023-00541-9
    online unter: https://arxiv.org/abs/2303.08549

17. D. Hinse, M. Thode, A. Rademacher, K. Pantke, C. Spura.
    Numerical identification of position-dependent friction coefficients from measured displacement data in a bolt-nut connection.
    , Volume 19, September 2023, 101214 , Elsevier, 2023.

    DOI: https://doi.org/10.1016/j.rineng.2023.101214

18. M. Höffmann, S. Patel, C. Büskens.
    Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints.
    MDPI Open Access Journals Agriculture, 13(11), 2023.

    DOI: 10.3390/agriculture13112112

19. D. Lorenz, F. Schneppe.
    Chambolle-Pock’s primal-dual method with mismatched adjoint.
    Applied Mathematics & Optimization, 87(22), 2023.

    DOI: 10.1007/s00245-022-09933-5
    online unter: https://arxiv.org/abs/2201.04928

20. D. Lorenz, F. Schneppe, L. Tondji.
    Linearly convergent adjoint free solution of least squares problems by random descent.
    Inverse Problems, , 2023.

    DOI: 10.1088/1361-6420/ad08ed
    online unter: https://arxiv.org/abs/2306.01946

21. D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenkötter, A. Rademacher, U. Iben, P. Maaß.
    Deep learning methods for partial differential equations and related parameter identification problems.
    Inverse Problems, 39(10), 2023.

    DOI: 10.1088/1361-6420/ace9d4

22. C. Nikolopoulos, M. Eden, A. Muntean.
    Multiscale simulation of colloids ingressing porous layers with evolving internal structure: A computational study.
    GEM -- International Journal on Geomathematics, 14(1), 19 p., 2023.

    DOI: 10.1007/s13137-022-00211-8

23. R. Ramirez Acosta, C. Wanigasekara, E. Frost, T. Brandt, S. Lehnhoff, C. Büskens.
    Integration of Intelligent Neighbourhood Grids to the German Distribution Grid: A Perspective.
    Energies, 16(11), 2023.

    DOI: 10.3390/en16114319

24. T. Shadbahr, M. Roberts, J. Stanczuk, J. Gilbey, P. Teare, S. Dittmer, M. Thorpe, R. V. Torne, E. Sala, P. Lio, M. Patel, .. AIX-COVNET Collaboration, J. H. F. Rudd, T. Mirtti, A. Rannikko, J. A. D. Aston, J. Tang, C. Schönlieb.
    The impact of imputation quality on machine learning classifiers for datasets with missing values.
    Communication medicine, 3, Springer Verlag, 2023.

    DOI: 10.1038/s43856-023-00356-z
    online unter: https://www.nature.com/articles/s43856-023-00356-z#citeas

25. M. Wichmann, M. Eden, D. Zvegincev, F. Wiesener, B. Bergmann, A. Schmidt.
    Modeling the wetting behavior of grinding wheels.
    The International Journal of Advanced Manufacturing Technology, 128, 1741–1747, 2023.

    DOI: 10.1007/s00170-023-12002-y

26. M. Wiesner, C. Büskens.
    Benchmarking solution methods for parameter identification in dynamical systems.
    PAMM, Proceedings in Applied Mathematics and Mechanics, e202300134 , Wiley, 2023.

    online unter: https://doi.org/10.1002/pamm.202300134