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

Arbeitsgruppe:AG Technomathematik
Leitung: Prof. Dr. Dr. h.c. Peter Maaß ((0421) 218-63801, E-Mail: pmaass@math.uni-bremen.de )
Bearbeitung: Dr. Tobias Kluth ((0421) 218-63817, E-Mail: tkluth@math.uni-bremen.de)
Sabine Eifeld (E-Mail: eifeld@math.uni-bremen.de )
Projektförderung: DFG
Projektpartner: 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
Laufzeit: 01.10.2016 - 31.03.2021
Webseite:https://www.uni-bremen.de/rtg-pi3
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Publikationen

  1. 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

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

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

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

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

  4. 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 unter: http://link.springer.com/article/10.1007/s10851-019-00923-x

  5. 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

  6. 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 unter: https://iopscience.iop.org/article/10.1088/1361-6420/ab1c66

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

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

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

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

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

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

  10. 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, Palermo, Italien.
  11. 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

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

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

  13. 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

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

    DOI: 10.1088/1361-6420/aac535

  15. 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, Deutschland.
  16. 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

  17. 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

  18. 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

  19. 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

  20. 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, Zypern.

    DOI: 10.23919/ECC.2018.8550045

  21. 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

  22. 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 unter: http://iopscience.iop.org/article/10.1088/1361-6420/aa7e07

  23. 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