DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications

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Publications

1. M. Beckmann, A. Bhandari, M. Iske.
   Fourier-Domain Inversion for the Modulo Radon Transform.
   IEEE Transactions on Computational Imaging, 10, 2024.

   online at: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10499837

2. M. Beckmann, N. Heilenkötter.
   Equivariant Neural Networks for Indirect Measurements.
   SIAM Journal on Mathematics of Data Science, 6(3), 2024.

   DOI: 10.1137/23M1582862
   online at: https://epubs.siam.org/doi/10.1137/23M1582862

3. 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 at: https://arxiv.org/abs/2308.14190

4. 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 at: https://iopscience.iop.org/article/10.1088/1361-6420/acce5e/meta

5. 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

6. 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

7. P. Fernsel, P. Maaß.
   Regularized Orthogonal Nonnegative Matrix Factorization and K-means Clustering.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2112.07641

8. P. Fernsel.
   Spatially Coherent Clustering Based on Orthogonal Nonnegative Matrix Factorization.
   MDPI Journal of Imaging, 7(10), 2021.

   DOI: 10.3390/jimaging7100194
   online at: https://www.mdpi.com/2313-433X/7/10/194

9. S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
   Regularization by architecture: A deep prior approach for inverse problems.
   Journal of Mathematical Imaging and Vision, 62(3):456-470, Springer Verlag, 2020.

   DOI: 10.1007/s10851-019-00923-x
   online at: http://link.springer.com/article/10.1007/s10851-019-00923-x

10. J. Jacobsen, J. Behrmann, N. Carlini, F. Tramer, N. Papernot.
    Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness.
    SafeML Workshop, ICLR, 2019.
    

    online at: https://arxiv.org/abs/1903.10484

11. S. Dittmer, E. King, P. Maaß.
    Singular values for ReLU layers.
    IEEE Transactions on Neural Networks and Learning Systems, Article , 2019.

    online at: https://ieeexplore.ieee.org/document/8891761

12. J. Clemens, T. Kluth, T. Reineking.
    β - SLAM: Simultaneous Localization an Grid Mapping with Beta Distributions.
    Information Fusion, 52:62-75, Elsevier, 2019.

    DOI: 10.1016/j.inffus.2018.11.005

13. A. Konschin, A. Lechleiter.
    Reconstruction of a Local Perturbation in Inhomogeneous Periodic Layers from Partial Near Field Measurements.
    Inverse Problems, 35(11), 114006, IOPscience, 2019.

    DOI: 10.1088/1361-6420/ab1c66
    online at: https://iopscience.iop.org/article/10.1088/1361-6420/ab1c66

14. J. Jacobsen, J. Behrmann, R. Zemel, M. Bethge.
    Excessive Invariance Causes Adversarial Vulnerability.
    International Conference on Learning Representations (ICLR), 2019.
    

    online at: https://openreview.net/forum?id=BkfbpsAcF7

15. J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte, J. Kriegsmann, P. Maaß.
    Deep Learning for Tumor Classification in Imaging Mass Spectrometry.
    Bioinformatics, 34(7):1215-1223, Oxford University Press, 2018.

    DOI: 10.1093/bioinformatics/btx724

16. T. Kluth.
    Mathematical models for magnetic particle imaging.
    Inverse Problems, 34(8), 2018.

    DOI: 10.1088/1361-6420/aac535

17. J. Clemens, C. Meerpohl, V. Schwarting, M. Rick, K. Schill, C. Büskens.
    Autonomous In-Ice Exploration of the Saturnian Moon Enceladus.
    69th International Astronautical Congress (IAC), 01.10.-05.10.2018, Bremen, Germany.
    
18. T. Kluth, B. Jin, G. Li.
    On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
    Inverse Problems, 34(9), 2018.

    DOI: 10.1088/1361-6420/aad015

19. D. Otero Baguer, P. Maaß.
    Inverse Problems in designing new structural materials.
    7th International Conference on High Performance Scientific Computing, 19.03-23.03.2018, Hanoi, Vietnam.
    

    DOI: 10.1007/978-3-030-55240-4_8

20. J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, P. Maaß.
    Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications.
    Bioinformatics, bty909 , 2018.

    DOI: 10.1093/bioinformatics/bty909

21. C. Meerpohl, K. Flaßkamp, C. Büskens.
    Optimization Strategies for Real-Time Control of an Autonomous Melting Probe.
    2018 American Control Conference (ACC), 2018, Milwaukee, WI, USA.
    

    DOI: 10.23919/ACC.2018.8430877

22. K. Schäfer, M. Runge, K. Flaßkamp, C. Büskens.
    Parameter Identification for Dynamical Systems Using Optimal Control Techniques.
    European Control Conference (ECC) 2018, 12.06.-15.06.2018, Limassol, Cyprus.
    

    DOI: 10.23919/ECC.2018.8550045

23. W. Heins, C. Büskens.
    Two-Level Forecast-Based Energy and Load Management for Grid-Connected Local Systems Using General Load and Storage Models.
    18th International Conference on Environment and Electrical Engineering (EEEIC), 12.06-15.06.2018, , Italy.
    
24. P. Fernsel, P. Maaß.
    A Survey on Surrogate Approaches to Non-negative Matrix Factorization.
    Vietnam Journal of Mathematics, 46(4):987-1021, Springer Verlag, 2018.

    DOI: 10.1007/s10013-018-0315-x

25. J. Behrmann, S. Dittmer, P. Fernsel, P. Maaß.
    Analysis of Invariance and Robustness via Invertibility of ReLU-Networks.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/1806.09730

26. C. Bathke, T. Kluth, C. Brandt, P. Maaß.
    Improved image reconstruction in magnetic particle imaging using structural a priori information.
    International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.

    DOI: 10.18416/ijmpi.2017.1703015

27. T. Kluth, P. Maaß.
    Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction.
    International Journal on Magnetic Particle Imaging, Article ID 1707004 3(2), 10 pages, 2017.

    DOI: 10.18416/ijmpi.2017.1707004

28. T. Gerken, A. Lechleiter.
    Reconstruction of a Time-dependent Potential from Wave Measurements.
    Inverse Problems, Article ID 094001 33(9), IOPscience, 2017.

    Ausgezeichnet als Highlight Paper

    DOI: 10.1088/1361-6420/aa7e07
    online at: http://iopscience.iop.org/article/10.1088/1361-6420/aa7e07