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Publikationen des Jahres 2020

Zeitschriftenartikel (24)

  1. E. Bänsch, A. Luttmann, J. Montalvo Urquizo, A. Schmidt, M. G. Villarreal-Marroquin.
    Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process.
    Journal of Mathematics in Industry, 10(2), 30 p., 2020.
  2. M. Beckmann, P. Maaß, J. Nickel.
    Error analysis for filtered back projection reconstructions in Besov spaces.
    Inverse Problems, 37 014002 37(1), IOPscience, 2020.
  3. T. Boskamp, D. Lachmund, R. Casadonte, L. Hauberg-Lotte, J. H. Kobarg, J. Kriegsmann, P. Maaß.
    Using the chemical noise background in MALDI mass spectrometry imaging for mass alignment and calibration.
    Analytical Chemistry, 92(1):1301-1308, 2020.

    DOI: 10.1021/acs.analchem.9b04473
    online unter: https://doi.org/10.1021/acs.analchem.9b04473

  4. I. Bougoudis, A. Blechschmidt, A. Richter, S. Seo, J. P. Burrows, N. Theys, A. Rinke.
    Long-term Time-series of Arctic Tropospheric BrO derived from UV-VIS Satellite Remote Sensing and its Relation to First Year Sea Ice.
    Erscheint in Atmospheric Chemistry and Physics

    DOI: 10.5194/acp-2020-116

  5. Y. Disser, J. Fearnley, M. Gairing, O. Göbel, M. Klimm, D. Schmand, A. Skopalik, A. Tönnis.
    Hiring Secretaries over Time: The Benefit of Concurrent Employment.
    Mathematics of Operations Research, 45(1):323-352, 2020.

    DOI: 10.1287/moor.2019.0993

  6. T. Gerken.
    Dynamic Inverse Wave Problems – Part II: Operator Identification and Applications.
    Inverse Problems, 36(2), IOPscience, 2020.

    DOI: 10.1088/1361-6420/ab47f4
    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ab47f4

  7. T. Gerken, S. Grützner.
    Dynamic Inverse Wave Problems – Part I: Regularity for the Direct Problem.
    Inverse Problems, 36(2), IOPscience, 2020.

    DOI: 10.1088/1361-6420/ab47ec
    online unter: https://iopscience.iop.org/article/10.1088/1361-6420/ab47ec

  8. H. Haddar, A. Konschin.
    Factorization Method for Imaging a Local Perturbation in Inhomogeneous Periodic Layers from Far Field Measurements.
    Inverse Problems and Imaging, 14(1):133-152, 2020.

    DOI: 10.3934/ipi.2019067
    online unter: https://www.aimsciences.org/article/doi/10.3934/ipi.2019067

  9. M. Jahn, J. Montalvo Urquizo.
    Modeling and simulation of keyhole-based welding as multi-domain problem using the extended finite element method.
    Applied Mathematical Modelling, 82:731-747, Elsevier, 2020.
  10. O. Klein, F. Fogt, S. Hollerbach, G. Nebrich, T. Boskamp, A. Wellmann.
    Classification of Inflammatory Bowel Disease from Formalin‐Fixed, Paraffin‐Embedded Tissue Biopsies via Imaging Mass Spectrometry.
    Proteomics - Clinical Applications, 190131 , Wiley, 2020.

    DOI: 10.1002/prca.201900131

  11. T. Kluth.
    Recent developments on system function/matrix representation, hybrid simulation techniques, and magnetic actuation.
    International Journal on Magnetic Particle Imaging, 6(1), 2020.

    DOI: https://journal.iwmpi.org/index.php/iwmpi/article/view/327

  12. T. Kluth, H. Albers.
    Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging.
    Erscheint in Oberwolfach Reports
  13. T. Kluth, C. Bathke, M. Jiang, P. Maaß.
    Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
    Inverse Problems, 36(12), 2020.

    DOI: https://doi.org/10.1088/1361-6420/abc2fe

  14. T. Kluth, B. Jin.
    L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
    International Journal on Magnetic Particle Imaging, , 2020.

    DOI: 10.18416/IJMPI.2020.2012001
    online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/146

  15. M. Lachmann, J. Maldonado, W. Bergmann, F. Jung, M. Weber, C. Büskens.
    Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System.
    Energies 2020, 13(8), 2084, 2020.

    DOI: 10.3390/en13082084

  16. F. Lieb, T. Boskamp, H. Stark.
    Peak detection for MALDI mass spectrometry imaging data using sparse frame multipliers.
    Journal of Proteomics, 103852 225, Elsevier, 2020.

    DOI: 10.1016/j.jprot.2020.103852

  17. T. H. Nguyen, D. Nho Hào, P. Maaß, L. Colombi Ciacchi.
    Mathematical aspects of catalyst positioning in lithium/air batteries.
    Inverse Problems, 36(4), 2020.

    DOI: 10.1088/1361-6420/ab47e6

  18. D. Otero Baguer, J. Leuschner, M. Schmidt.
    Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.
    Inverse Problems, 36(9), IOPscience, 2020.

    DOI: 10.1088/1361-6420/aba415

  19. G. Rigaud, B. Hahn.
    Reconstruction Algorithm For 3D Compton Scattering Imaging With Incomplete Data.
    Erscheint in Inverse Problems in Science and Engineering
  20. L. Siemer, I. Ovsyannikov, J. Rademacher.
    Inhomogeneous domain walls in spintronic nanowires.
    Nonlinearity, 2905 33(6), IOPscience, 2020.

    DOI: 10.1088/1361-6544/ab6f4e

  21. 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

  22. M. Steinherr Zazo, J. Rademacher.
    Lyapunov coefficients for Hopf bifurcations in systems with piecewise smooth nonlinearity.
    Erscheint in SIAM Journal on Applied Dynamical Systems

    online unter: https://arxiv.org/abs/2006.07085

  23. J. von Schroeder, T. Dickhaus.
    Efficient Calculation of the Joint Distribution of Order Statistics.
    Computational Statistics & Data Analysis, 144, Elsevier, 2020.

    DOI: 10.1016/j.csda.2019.106899

  24. M. Wolff.
    On Build-up of Epidemiologic Models - Development of a SEI^3RSD model for the Spread of SARS-CoV-2.
    ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, , 36 S., WILEY-VCH, 2020.

    DOI: 10.1002/zamm.202000230

Tagungsbeiträge (13)

  1. H. Albers, T. Kluth, T. Knopp.
    MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
    10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
    International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):3 pages, Infinite Science Publishing, 2020.

    DOI: 10.18416/IJMPI.2020.2009020

  2. N. Bertschinger, M. Hoefer, D. Schmand.
    Strategic Payments in Financial Networks.
    11th Innovations in Theoretical Computer Science Conference (ITCS 2020).

    DOI: 10.4230/LIPIcs.ITCS.2020.46

  3. A. Denker, M. Schmidt, J. Leuschner, P. Maaß, J. Behrmann.
    Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction.
    ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 18.07-18.07.2020, Wien, Österreich.

    online unter: https://invertibleworkshop.github.io/accepted_papers/index.html

  4. S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
    A Deep Prior Approach to Magnetic Particle Imaging.
    Machine Learning for Medical Image Reconstruction 2020.
    Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Hrsg.), S. 113-122, 2020.

    DOI: 10.1007/978-3-030-61598-7_11

  5. L. Evers.
    Benchmarking pre-trained Encoders for real-time Semantic Road Scene Segmentation.
    GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
    Proceedings in Applied Mathematics & Mechanics, 20(1), WILEY-VCH, 2020.

    DOI: 10.1002/pamm.202000275

  6. T. Kluth, P. Szwargulski, T. Knopp.
    Towards accurate modeling of the multidimensional MPI physics.
    10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
    International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):2 pages, Infinite Science Publishing, 2020.

    DOI: 10.18416/IJMPI.2020.2009004

  7. M. Knauer, C. Büskens.
    The Augmented Reality Sandbox Makes Optimization Visible.
    21th IFAC World Congress, 11.07.-17.07.2020, Berlin, Deutschland.
    Proceedings of the 21th IFAC World Congress, 53(2):17572-17577, 2020.

    DOI: 10.1016/j.ifacol.2020.12.2670

  8. M. Lachmann, F. Jung, C. Büskens.
    Computationally efficient identification of databased models applied to a milk cooling system.
    Conference of Computational Interdisciplinary Science, CCIS, 19.03.-22.03.2019, Atlanta, USA.
    Campinas: Galoa, 2020.

    online unter: https://proceedings.science/ccis-2019/papers/computationally-efficient-identification-of-databased-models-applied-to-a-milk-cooling-system

  9. M. Möddel, F. Griese, T. Kluth, T. Knopp.
    Estimating orientation using multi-contrast MPI.
    10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
    International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):3 pages, Infinite Science Publishing, 2020.

    DOI: 10.18416/IJMPI.2020.2009023

  10. I. Mykhailiuk, K. Flaßkamp, C. Büskens, K. Schäfer.
    On the Computation of Convergence Regions for Sequential Nonlinear Programming Problems.
    GAMM 92st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
    Proceedings in Applied Mathematics & Mechanics, 20(1), WILEY-VCH, 2020.

    DOI: 10.1002/pamm.202000281

  11. I. Mykhailiuk, K. Schäfer, K. Flaßkamp, C. Büskens.
    Preferable Minima in Nonlinear Optimization: Definition and Algorithmic Approaches.
    IEEE 6th International Conference on Robotic Computing (IRC), 05.12.-07.12.2022.
    Erscheint in Proceedings of International Conference on Robotic Computing.

    online unter: https://hessenbox.uni-kassel.de/dl/fi226HzF3AJV3g4LFWM4fWE6/daily_program_2020.pdf?inline

  12. M. Runge, K. Flaßkamp, C. Büskens.
    Model Predictive Control with Online Nonlinear Parameter Identification for a Robotic System.
    International Conference on Control, Decision and Information Technologies (CoDIT), 29.06.-02.07.2020, Prag, Tschechien.
    Proceedings of CoDIT, 1:312-318, 2020.

    DOI: 10.1109/CoDIT49905.2020.9263886

  13. F. Tramer, J. Behrmann, N. Carlini, N. Papernot, J. Jacobsen.
    Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations.
    International Conference on Machine Learning (ICML), 12.07 - 18.07.2020, Wien, Österreich.

    online unter: https://arxiv.org/abs/2002.04599

Buchkapitel (4)

  1. E. Bänsch, A. Schmidt.
    Free boundary problems in fluids and materials.
    Geometric Partial Differential Equations - Part I, A. Bonito, R. H. Nochetto (Hrsg.), Handbook of Numerical Analysis Vol. 21, S. 555-619, Elsevier, 2020.
  2. T. Gerken.
    Dynamic Inverse Problems for the Acoustic Wave Equation.
    Time-dependent Problems in Imaging and Parameter Identification, T. Schuster, B. Kaltenbacher, A. Wald (Hrsg.), S. 25-50, Springer Verlag, 2020.

    DOI: 10.1007/978-3-030-57784-1

  3. S. Görres, S. Böttcher, P. Rink, W. Brannath.
    StaVaCare 2.0 - Zusammenhänge zwischen Care-, Case-Mix, Organisation und Qualität in Pflegeheimen.
    Schriftenreihe zur Weiterentwicklung der Pflegeversicherung, GKV Spitzenverband, 2020.
  4. B. Kuhfuß, C. Schattmann, M. Jahn, A. Schmidt, F. Vollertsen, E. Moumi, C. Schenck, M. Herrmann, S. Ishkina, L. Rathmann, L. Heinrich.
    Micro Forming Processes.
    Cold Micro Metal Forming, F. Vollertsen, S. Friedrich, B. Kuhfuß, P. Maaß, C. Thomy, H. Zoch (Hrsg.), Lecture Notes in Production Engineering, S. 27-94, Springer Verlag, 2020.

Qualifikationsarbeiten (7)

  1. J. Benke.
    Adaptive Gittergenerierung für das Finite-Elemente-Ozean- und Meeresmodell FESOM.
    Dissertationsschrift, Universität Bremen, 2020.

    DOI: 10.26092/elib/243

  2. C. Brandt.
    Recurrence Quantification Compared to Fourier Analysis for Ultrasonic Non-Destructive Testing of Fibre Reinforced Polymers.
    Dissertationsschrift, Universität Bremen, 2020.
  3. S. Dittmer.
    On deep learning applied to inverse problems - A chicken-and-egg problem.
    Dissertationsschrift, Universität Bremen, 2020.
  4. C. Etmann.
    Double Backpropagation with Applications to Robustness and Saliency Map Interpretability.
    Dissertationsschrift, Universität Bremen, 2020.
  5. G. Fragoso Trigo.
    Low-cost failure-tolerant hybrid navigation designs for future space transportation systems.
    Dissertationsschrift, Universität Bremen, 2020.

    DOI: 10.26092/elib/472

  6. F. Kohlmai.
    Modellierung, Parameteridentifikation und optimale Drehzahlregelung eines Schiffsmotors im Gasbetrieb.
    Dissertationsschrift, Universität Bremen, 2020.

    DOI: 10.26092/elib/375

  7. D. Otero Baguer.
    Neural Networks for solving Inverse Problems. Applications in Materials Science and Medical Imaging. (submitted).
    Dissertationsschrift, Universität Bremen, 2020.

Preprints (5)

  1. J. Behrmann, P. Vicol, K. Wang, R. Grosse, J. Jacobsen.
    Understanding and Mitigating Exploding Inverses in Invertible Neural Networks.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2006.09347

  2. L. Kuger, G. Rigaud.
    Joint fan-beam CT and Compton scattering tomography: analysis and image reconstruction.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2008.06699

  3. S. . Mukherjee, S. Dittmer, Z. . Shumaylov, S. Lunz, O. Öktem, C. Schönlieb.
    Learned convex regularizers for inverse problems.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2008.02839

  4. I. Piotrowska-Kurczewski, G. Sfakianaki.
    Tikhonov functionals with a tolerance measure introduced in the regularization.
    Zur Veröffentlichung eingereicht.

    online unter: http://arxiv.org/abs/2007.06431

  5. J. von Schroeder.
    Stable Feature Selection with Applications to MALDI Imaging Mass Spectrometry Data.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2006.15077