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Publikationen des Jahres 2020

Zeitschriftenartikel (22)

  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. 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, :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

  7. 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

  8. 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

  9. 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

  10. 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.
  11. 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

  12. 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 124006 36(12), 2020.

    online unter: https://arxiv.org/abs/2004.13091

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

    online unter: https://arxiv.org/abs/2001.06083

  14. 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

  15. 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

  16. 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

  17. 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: https://doi.org/10.1088/1361-6420/aba415

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

    DOI: 10.1088/1361-6544/ab6f4e

  20. 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

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

    online unter: https://doi.org/10.1016/j.csda.2019.106899

  22. 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.

    online unter: https://doi.org/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.
    Erscheint in Infinite Science Publishing, T. Knopp, T. M. Buzug (Hrsg.), S. 2 pages.
  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.
  5. L. Evers.
    Benchmarking pre-trained Encoders for real-time Semantic Road Scene Segmentation.
    GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
  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.
    Erscheint in Infinite Science Publishing, T. Knopp, T. M. Buzug (Hrsg.), S. 2 pages.
  7. 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

  8. 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.
    Erscheint in Infinite Science Publishing, T. Knopp, T. M. Buzug (Hrsg.), S. 2 pages.
  9. I. Mykhailiuk, K. Flaßkamp, C. Büskens, K. Schäfer.
    On the Computation of Convergence Regions for Sequential Nonlinear Programming Problems.
    GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
  10. I. Mykhailiuk, K. Schäfer, K. Flaßkamp, C. Büskens.
    Preferable Minima in Nonlinear Optimization: Definition and Algorithmic Approaches.
    9th International Conference on Algorithms and Complexity (CIAC 2015).

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

  11. 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.
    Erscheint in Proceedings of CoDIT.
  12. M. Runge, K. Flaßkamp, C. Büskens.
    Real-time parameter estimation for sensitivity-based LQ regulator adaptation .
    GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
  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.

    online unter: https://doi.org/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 (9)

  1. H. Albers, T. Kluth, T. Knopp.
    A simulation framework for particle magnetization dynamics of large ensembles of single domain particles: Numerical treatment of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2010.07772

  2. S. Arridge, P. Fernsel, A. Hauptmann.
    Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2005.14042

  3. 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

  4. S. Dittmer, T. Kluth, M. Henriksen, P. Maaß.
    Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
    Zur Veröffentlichung eingereicht.

    online unter: https://arxiv.org/abs/2007.01593

  5. S. Dittmer, C. Schönlieb, P. Maaß.
    Ground Truth Free Denoising by Optimal Transport.
    Zur Veröffentlichung eingereicht.

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

  6. 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

  7. 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

  8. 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

  9. 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