Publikationen der AG Technomathematik

Monografien (1)

1. H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.).
   German Success Stories in Industrial Mathematics .
   Mathematics in Industrie, 167 Seiten, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7

Zeitschriftenartikel (10)

1. R. Barbano, J. Leuschner, M. Schmidt, A. Denker, A. Hauptmann, P. Maaß, B. Jin.
   An Educated Warm Start For Deep Image Prior-based Micro CT Reconstruction.
   IEEE Transactions on Computational Imaging, 8:1210-1222, 2022.

   DOI: 10.1109/TCI.2022.3233188

2. S. Dittmer, C. Schönlieb, P. Maaß.
   Ground Truth Free Denoising by Optimal Transport.
   Erscheint in Numerical Algebra, Control, and Optimization

   online unter: https://arxiv.org/abs/2007.01575

3. C. Arndt, A. Denker, J. Nickel, J. Leuschner, M. Schmidt, G. Rigaud.
   In Focus - hybrid deep learning approaches to the HDC2021 challenge.
   Inverse Problems and Imaging, , 2022.

   DOI: 10.3934/ipi.2022061

4. S. Arridge, P. Fernsel, A. Hauptmann.
   Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems.
   Inverse Problems and Imaging, 16(3):483-523, 2022.

   DOI: 10.3934/ipi.2021059

5. D. Nganyu Tanyu, D. Schulz, T. Tatietse, T. Lukong.
   Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System.
   Energy and Environment Research, 12(1), 2022.

   DOI: 10.5539/eer.v12n1p45
   online unter: https://ccsenet.org/journal/index.php/eer/article/view/0/47276

6. H. Albers, T. Knopp, M. Möddel, M. Boberg, T. Kluth.
   Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging.
   Journal of Magnetism and Magnetic Materials, 543, 168534, Elsevier, 2022.

   DOI: 10.1016/j.jmmm.2021.168534

7. C. Arndt.
   Regularization Theory of the Analytic Deep Prior Approach.
   Inverse Problems, 38(11), 2022.

   DOI: 10.1088/1361-6420/ac9011

8. C. Janßen, T. Boskamp, L. Hauberg-Lotte, J. Behrmann, S. Deininger, M. Kriegsmann, K. Kriegsmann, G. Steinbuß, H. Winter, T. Muley, R. Casadonte, J. Kriegsmann, P. Maaß.
   Robust subtyping of non-small cell lung cancer whole sections through MALDI mass spectrometry imaging.
   Proteomics - Clinical Applications, PRCA2208 , 2022.

   DOI: 10.1002/prca.202100068

9. H. Albers, T. Kluth, T. Knopp.
   Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging.
   Journal of Magnetism and Magnetic Materials, 541, 168508, Elsevier, 2022.

   DOI: 10.1016/j.jmmm.2021.168508
   online unter: https://www.sciencedirect.com/science/article/abs/pii/S0304885321007678

10. M. Beckmann, A. Bhandari, F. Krahmer.
    The Modulo Radon Transform: Theory, Algorithms and Applications.
    SIAM Journal on Imaging Sciences, 15(2):455-490, 2022.

    DOI: 10.1137/21M1424615

Tagungsbeiträge (3)

1. M. Nitzsche, H. Albers, T. Kluth, B. Hahn.
   Compensating model imperfections during image reconstruction via resesop.
   International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
   International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.

   DOI: 10.18416/IJMPI.2022.2203062

2. H. Albers, T. Kluth.
   Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional lissajous-type excitation: An equilibrium model approach.
   International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
   International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.

   DOI: 10.18416/IJMPI.2022.2203048

3. M. Beckmann, A. Bhandari.
   MR. TOMP: Inversion of the Modulo Radon Transform (MRT) via Orthogonal Matching Pursuit (OMP).
   2022 IEEE International Conference on Image Processing (ICIP), 16.10.-19.10.2022.
   

Buchkapitel (3)

1. P. Maaß, L. Hauberg-Lotte, T. Boskamp.
   MALDI Imaging: Exploring the Molecular Landscape.
   German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.), Mathematics in Industry, S. 97-103, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7_17

2. P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
   Mathematische Architekturen für Neuronale Netze.
   Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.

   DOI: 10.1007/s00591-022-00325-y

3. J. Behrmann, M. Schmidt, J. Wildner, P. Maaß, S. Schmale.
   Purity Assessment of Pellets Using Deep Learning.
   German Success Stories in Industrial Mathematics, H. Bock, K. Küfer, P. Maaß, A. Milde, V. Schulz (Hrsg.), Mathematics in Industry, S. 29-34, Springer Verlag, 2022.

   DOI: 10.1007/978-3-030-81455-7_6

Qualifikationsarbeiten (2)

1. M. Schmidt.
   Hybrid Deep Learning: How Combining Data-Driven and Model-Based Approaches Solves Inverse Problems in Computed Tomography and Beyond.
   Dissertationsschrift, Universität Bremen, 2022.

   DOI: 10.26092/elib/1941

2. P. Fernsel.
   Nonnegative Matrix Factorization: Theory, Algorithms and Applications.
   Dissertationsschrift, Universität Bremen, 2022.

   DOI: 10.26092/elib/1493
   online unter: https://doi.org/10.26092/elib/1493

Preprints (3)

1. R. Barbano, J. Leuschner, J. Antorán, B. Jin, J. M. Hernández-Lobato.
   Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2207.05714

2. S. Dittmer, D. Erzmann, H. Harms, P. Maaß.
   SELTO: Sample-Efficient Learned Topology Optimization.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2209.05098

3. T. Grossmann, S. Dittmer, Y. Korolev, C. Schönlieb.
   Unsupervised Learning of the Total Variation Flow.
   Zur Veröffentlichung eingereicht.

   online unter: https://arxiv.org/abs/2206.04406#