Logo Uni Bremen

Center for Industrial Mathematics

ZeTeM > Research and Applications > Projects > DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications

Contact Sitemap Impressum [ English | Deutsch ]

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

Working Group:WG Industrial Mathematics
Leadership: Prof. Dr. Dr. h.c. Peter Maaß ((0421) 218-63801, E-Mail: pmaass@math.uni-bremen.de )
Processor: Dr. Tobias Kluth
Sabine Eifeld (E-Mail: eifeld@math.uni-bremen.de )
Funding: DFG
Project partner: Prof. Dr. Armin Lechleiter, Universität Bremen
Prof. Dr. Werner Brannath, Kompetenzzentrum für Klinische Studien Bremen
Prof. Dr. Christof Büskens, Universität Bremen
Prof. Dr. Alfred Schmidt, Universität Bremen
Prof. Dr. Emily King, Universität Bremen
Prof. Dr. Jens Rademacher, Universität Bremen
Prof. Dr. Dmitry Feichtner-Kozlov, Universität Bremen
Dr. Iwona Piotrowska-Kurczewski, Universität Bremen
Time period: 01.10.2016 - 31.03.2021
Website:https://www.uni-bremen.de/rtg-pi3
Bild des Projekts DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications You are redirected to the webpage of the research training group. If this does not work properly, please click here.

Publications

  1. M. Beckmann, N. Heilenkötter.
    Equivariant Neural Networks for Indirect Measurements.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2306.16506

  2. 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

  3. 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

  4. 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

  5. M. Beckmann, A. Bhandari, M. Iske.
    Fourier-Domain Inversion for the Modulo Radon Transform.
    Zur Veröffentlichung eingereicht.

    online at: https://arxiv.org/abs/2307.13114

  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.
    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

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

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

  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. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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.
  19. 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

  20. 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

  21. 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

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

    DOI: 10.1088/1361-6420/aac535

  23. 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.
  24. 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

  25. 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

  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